
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_stack_predictors.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_ensemble_plot_stack_predictors.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_stack_predictors.py:


=================================
Combine predictors using stacking
=================================

.. currentmodule:: sklearn

Stacking refers to a method to blend estimators. In this strategy, some
estimators are individually fitted on some training data while a final
estimator is trained using the stacked predictions of these base estimators.

In this example, we illustrate the use case in which different regressors are
stacked together and a final linear penalized regressor is used to output the
prediction. We compare the performance of each individual regressor with the
stacking strategy. Stacking slightly improves the overall performance.

.. GENERATED FROM PYTHON SOURCE LINES 18-22

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 23-39

Download the dataset
####################

We will use the `Ames Housing`_ dataset which was first compiled by Dean De Cock
and became better known after it was used in Kaggle challenge. It is a set
of 1460 residential homes in Ames, Iowa, each described by 80 features. We
will use it to predict the final logarithmic price of the houses. In this
example we will use only 20 most interesting features chosen using
GradientBoostingRegressor() and limit number of entries (here we won't go
into the details on how to select the most interesting features).

The Ames housing dataset is not shipped with scikit-learn and therefore we
will fetch it from `OpenML`_.

.. _`Ames Housing`: http://jse.amstat.org/v19n3/decock.pdf
.. _`OpenML`: https://www.openml.org/d/42165

.. GENERATED FROM PYTHON SOURCE LINES 39-84

.. code-block:: Python


    import numpy as np

    from sklearn.datasets import fetch_openml
    from sklearn.utils import shuffle


    def load_ames_housing():
        df = fetch_openml(name="house_prices", as_frame=True)
        X = df.data
        y = df.target

        features = [
            "YrSold",
            "HeatingQC",
            "Street",
            "YearRemodAdd",
            "Heating",
            "MasVnrType",
            "BsmtUnfSF",
            "Foundation",
            "MasVnrArea",
            "MSSubClass",
            "ExterQual",
            "Condition2",
            "GarageCars",
            "GarageType",
            "OverallQual",
            "TotalBsmtSF",
            "BsmtFinSF1",
            "HouseStyle",
            "MiscFeature",
            "MoSold",
        ]

        X = X.loc[:, features]
        X, y = shuffle(X, y, random_state=0)

        X = X.iloc[:600]
        y = y.iloc[:600]
        return X, np.log(y)


    X, y = load_ames_housing()








.. GENERATED FROM PYTHON SOURCE LINES 85-91

Make pipeline to preprocess the data
####################################

Before we can use Ames dataset we still need to do some preprocessing.
First, we will select the categorical and numerical columns of the dataset to
construct the first step of the pipeline.

.. GENERATED FROM PYTHON SOURCE LINES 91-98

.. code-block:: Python


    from sklearn.compose import make_column_selector

    cat_selector = make_column_selector(dtype_include=[object, "string"])
    num_selector = make_column_selector(dtype_include=np.number)
    cat_selector(X)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ['HeatingQC', 'Street', 'Heating', 'MasVnrType', 'Foundation', 'ExterQual', 'Condition2', 'GarageType', 'HouseStyle', 'MiscFeature']



.. GENERATED FROM PYTHON SOURCE LINES 99-101

.. code-block:: Python

    num_selector(X)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ['YrSold', 'YearRemodAdd', 'BsmtUnfSF', 'MasVnrArea', 'MSSubClass', 'GarageCars', 'OverallQual', 'TotalBsmtSF', 'BsmtFinSF1', 'MoSold']



.. GENERATED FROM PYTHON SOURCE LINES 102-111

Then, we will need to design preprocessing pipelines which depends on the
ending regressor. If the ending regressor is a linear model, one needs to
one-hot encode the categories. If the ending regressor is a tree-based model
an ordinal encoder will be sufficient. Besides, numerical values need to be
standardized for a linear model while the raw numerical data can be treated
as is by a tree-based model. However, both models need an imputer to
handle missing values.

We will first design the pipeline required for the tree-based models.

.. GENERATED FROM PYTHON SOURCE LINES 111-129

.. code-block:: Python


    from sklearn.compose import make_column_transformer
    from sklearn.impute import SimpleImputer
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import OrdinalEncoder

    cat_tree_processor = OrdinalEncoder(
        handle_unknown="use_encoded_value",
        unknown_value=-1,
        encoded_missing_value=-2,
    )
    num_tree_processor = SimpleImputer(strategy="mean", add_indicator=True)

    tree_preprocessor = make_column_transformer(
        (num_tree_processor, num_selector), (cat_tree_processor, cat_selector)
    )
    tree_preprocessor






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        background-size: 14px 14px;
        background-position: 0;
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        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-21" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                     SimpleImputer(add_indicator=True),
                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                    (&#x27;ordinalencoder&#x27;,
                                     OrdinalEncoder(encoded_missing_value=-2,
                                                    handle_unknown=&#x27;use_encoded_value&#x27;,
                                                    unknown_value=-1),
                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-72" type="checkbox" ><label for="sk-estimator-id-72" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content " data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;simpleimputer&#x27;, ...), (&#x27;ordinalencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-73" type="checkbox" ><label for="sk-estimator-id-73" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content " data-param-prefix="simpleimputer__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-74" type="checkbox" ><label for="sk-estimator-id-74" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-75" type="checkbox" ><label for="sk-estimator-id-75" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="ordinalencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-76" type="checkbox" ><label for="sk-estimator-id-76" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="ordinalencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values, and should be sorted in case of numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27use_encoded_value%27%7D%2C%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'use_encoded_value'}, default='error'<br><br>When set to 'error' an error will be raised in case an unknown<br>categorical feature is present during transform. When set to<br>'use_encoded_value', the encoded value of unknown categories will be<br>set to the value given for the parameter `unknown_value`. In<br>:meth:`inverse_transform`, an unknown category will be denoted as None.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">&#x27;use_encoded_value&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('unknown_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=unknown_value,-int%20or%20np.nan%2C%20default%3DNone">
                unknown_value
                <span class="param-doc-description">unknown_value: int or np.nan, default=None<br><br>When the parameter handle_unknown is set to 'use_encoded_value', this<br>parameter is required and will set the encoded value of unknown<br>categories. It has to be distinct from the values used to encode any of<br>the categories in `fit`. If set to np.nan, the `dtype` parameter must<br>be a float dtype.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('encoded_missing_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=encoded_missing_value,-int%20or%20np.nan%2C%20default%3Dnp.nan">
                encoded_missing_value
                <span class="param-doc-description">encoded_missing_value: int or np.nan, default=np.nan<br><br>Encoded value of missing categories. If set to `np.nan`, then the `dtype`<br>parameter must be a float dtype.<br><br>.. versionadded:: 1.1</span>
            </a>
        </td>
                <td class="value">-2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output categories for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>`max_categories` do **not** take into account missing or unknown<br>categories. Setting `unknown_value` or `encoded_missing_value` to an<br>integer will increase the number of unique integer codes by one each.<br>This can result in up to `max_categories + 2` integer codes.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {
        // Get the parameter prefix from the closest toggleable content
        const toggleableContent = element.closest('.sk-toggleable__content');
        const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
        const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;

        const originalStyle = element.style;
        const computedStyle = window.getComputedStyle(element);
        const originalWidth = computedStyle.width;
        const originalHTML = element.innerHTML.replace('Copied!', '');

        navigator.clipboard.writeText(fullParamName)
            .then(() => {
                element.style.width = originalWidth;
                element.style.color = 'green';
                element.innerHTML = "Copied!";

                setTimeout(() => {
                    element.innerHTML = originalHTML;
                    element.style = originalStyle;
                }, 2000);
            })
            .catch(err => {
                console.error('Failed to copy:', err);
                element.style.color = 'red';
                element.innerHTML = "Failed!";
                setTimeout(() => {
                    element.innerHTML = originalHTML;
                    element.style = originalStyle;
                }, 2000);
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        return false;
    }

    document.querySelectorAll('.copy-paste-icon').forEach(function(element) {
        const toggleableContent = element.closest('.sk-toggleable__content');
        const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
        const paramName = element.parentElement.nextElementSibling
            .textContent.trim().split(' ')[0];
        const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;

        element.setAttribute('title', fullParamName);
    });


    /**
     * Adapted from Skrub
     * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
     * @returns "light" or "dark"
     */
    function detectTheme(element) {
        const body = document.querySelector('body');

        // Check VSCode theme
        const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
        const themeNameAttr = body.getAttribute('data-vscode-theme-name');

        if (themeKindAttr && themeNameAttr) {
            const themeKind = themeKindAttr.toLowerCase();
            const themeName = themeNameAttr.toLowerCase();

            if (themeKind.includes("dark") || themeName.includes("dark")) {
                return "dark";
            }
            if (themeKind.includes("light") || themeName.includes("light")) {
                return "light";
            }
        }

        // Check Jupyter theme
        if (body.getAttribute('data-jp-theme-light') === 'false') {
            return 'dark';
        } else if (body.getAttribute('data-jp-theme-light') === 'true') {
            return 'light';
        }

        // Guess based on a parent element's color
        const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
        const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
        if (match) {
            const [r, g, b] = [
                parseFloat(match[1]),
                parseFloat(match[2]),
                parseFloat(match[3])
            ];

            // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
            const luma = 0.299 * r + 0.587 * g + 0.114 * b;

            if (luma > 180) {
                // If the text is very bright we have a dark theme
                return 'dark';
            }
            if (luma < 75) {
                // If the text is very dark we have a light theme
                return 'light';
            }
            // Otherwise fall back to the next heuristic.
        }

        // Fallback to system preference
        return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
    }


    function forceTheme(elementId) {
        const estimatorElement = document.querySelector(`#${elementId}`);
        if (estimatorElement === null) {
            console.error(`Element with id ${elementId} not found.`);
        } else {
            const theme = detectTheme(estimatorElement);
            estimatorElement.classList.add(theme);
        }
    }

    forceTheme('sk-container-id-21');</script></body>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 130-132

Then, we will now define the preprocessor used when the ending regressor
is a linear model.

.. GENERATED FROM PYTHON SOURCE LINES 132-145

.. code-block:: Python


    from sklearn.preprocessing import OneHotEncoder, StandardScaler

    cat_linear_processor = OneHotEncoder(handle_unknown="ignore")
    num_linear_processor = make_pipeline(
        StandardScaler(), SimpleImputer(strategy="mean", add_indicator=True)
    )

    linear_preprocessor = make_column_transformer(
        (num_linear_processor, num_selector), (cat_linear_processor, cat_selector)
    )
    linear_preprocessor






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-22 {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: #000;
      --sklearn-color-text-muted: #666;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;
    }

    #sk-container-id-22.light {
      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: black;
      --sklearn-color-background: white;
      --sklearn-color-border-box: black;
      --sklearn-color-icon: #696969;
    }

    #sk-container-id-22.dark {
      --sklearn-color-text-on-default-background: white;
      --sklearn-color-background: #111;
      --sklearn-color-border-box: white;
      --sklearn-color-icon: #878787;
    }

    #sk-container-id-22 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-22 pre {
      padding: 0;
    }

    #sk-container-id-22 input.sk-hidden--visually {
      border: 0;
      clip: rect(1px 1px 1px 1px);
      clip: rect(1px, 1px, 1px, 1px);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
    }

    #sk-container-id-22 div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
      padding-bottom: 0.4em;
      background-color: var(--sklearn-color-background);
    }

    #sk-container-id-22 div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-22 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    #sk-container-id-22 div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    #sk-container-id-22 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-22 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-22 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-22 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-22 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-22 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    clickable and can be expanded/collapsed.
    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-22 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-22 label.sk-toggleable__label {
      cursor: pointer;
      display: flex;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
      align-items: center;
      justify-content: center;
      gap: 0.5em;
    }

    #sk-container-id-22 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-22 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-22 div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-22 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-22 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-22 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-22 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-22 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-22 div.sk-label label.sk-toggleable__label,
    #sk-container-id-22 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-22 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-22 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-22 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-22 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-22 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-22 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-22 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-3) 1pt solid;
      color: var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3) 1pt solid;
      color: var(--sklearn-color-fitted-level-3);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-unfitted-level-0);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-fitted-level-0);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-22 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-22 a.estimator_doc_link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-22 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-22 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }

    .estimator-table {
        font-family: monospace;
    }

    .estimator-table summary {
        padding: .5rem;
        cursor: pointer;
    }

    .estimator-table summary::marker {
        font-size: 0.7rem;
    }

    .estimator-table details[open] {
        padding-left: 0.1rem;
        padding-right: 0.1rem;
        padding-bottom: 0.3rem;
    }

    .estimator-table .parameters-table {
        margin-left: auto !important;
        margin-right: auto !important;
        margin-top: 0;
    }

    .estimator-table .parameters-table tr:nth-child(odd) {
        background-color: #fff;
    }

    .estimator-table .parameters-table tr:nth-child(even) {
        background-color: #f6f6f6;
    }

    .estimator-table .parameters-table tr:hover {
        background-color: #e0e0e0;
    }

    .estimator-table table td {
        border: 1px solid rgba(106, 105, 104, 0.232);
    }

    /*
        `table td`is set in notebook with right text-align.
        We need to overwrite it.
    */
    .estimator-table table td.param {
        text-align: left;
        position: relative;
        padding: 0;
    }

    .user-set td {
        color:rgb(255, 94, 0);
        text-align: left !important;
    }

    .user-set td.value {
        color:rgb(255, 94, 0);
        background-color: transparent;
    }

    .default td {
        color: black;
        text-align: left !important;
    }

    .user-set td i,
    .default td i {
        color: black;
    }

    /*
        Styles for parameter documentation links
        We need styling for visited so jupyter doesn't overwrite it
    */
    a.param-doc-link,
    a.param-doc-link:link,
    a.param-doc-link:visited {
        text-decoration: underline dashed;
        text-underline-offset: .3em;
        color: inherit;
        display: block;
        padding: .5em;
    }

    /* "hack" to make the entire area of the cell containing the link clickable */
    a.param-doc-link::before {
        position: absolute;
        content: "";
        inset: 0;
    }

    .param-doc-description {
        display: none;
        position: absolute;
        z-index: 9999;
        left: 0;
        padding: .5ex;
        margin-left: 1.5em;
        color: var(--sklearn-color-text);
        box-shadow: .3em .3em .4em #999;
        width: max-content;
        text-align: left;
        max-height: 10em;
        overflow-y: auto;

        /* unfitted */
        background: var(--sklearn-color-unfitted-level-0);
        border: thin solid var(--sklearn-color-unfitted-level-3);
    }

    /* Fitted state for parameter tooltips */
    .fitted .param-doc-description {
        /* fitted */
        background: var(--sklearn-color-fitted-level-0);
        border: thin solid var(--sklearn-color-fitted-level-3);
    }

    .param-doc-link:hover .param-doc-description {
        display: block;
    }

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-22" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                     Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                      StandardScaler()),
                                                     (&#x27;simpleimputer&#x27;,
                                                      SimpleImputer(add_indicator=True))]),
                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                    (&#x27;onehotencoder&#x27;,
                                     OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-77" type="checkbox" ><label for="sk-estimator-id-77" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for ColumnTransformer</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content " data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;pipeline&#x27;, ...), (&#x27;onehotencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-78" type="checkbox" ><label for="sk-estimator-id-78" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content " data-param-prefix="pipeline__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-79" type="checkbox" ><label for="sk-estimator-id-79" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="pipeline__standardscaler__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_mean',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue">
                with_mean
                <span class="param-doc-description">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_std',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue">
                with_std
                <span class="param-doc-description">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-80" type="checkbox" ><label for="sk-estimator-id-80" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="pipeline__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-81" type="checkbox" ><label for="sk-estimator-id-81" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="onehotencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-82" type="checkbox" ><label for="sk-estimator-id-82" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="onehotencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('drop',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone">
                drop
                <span class="param-doc-description">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_output',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue">
                sparse_output
                <span class="param-doc-description">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in "Compressed Sparse Row" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown="infrequent_if_exist"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `"warn"` was added in 1.6.</span>
            </a>
        </td>
                <td class="value">&#x27;ignore&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('feature_name_combiner',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22">
                feature_name_combiner
                <span class="param-doc-description">feature_name_combiner: "concat" or callable, default="concat"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`"concat"` concatenates encoded feature name and category with<br>`feature + "_" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>
            </a>
        </td>
                <td class="value">&#x27;concat&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
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.. GENERATED FROM PYTHON SOURCE LINES 146-164

Stack of predictors on a single data set
########################################

It is sometimes tedious to find the model which will best perform on a given
dataset. Stacking provide an alternative by combining the outputs of several
learners, without the need to choose a model specifically. The performance of
stacking is usually close to the best model and sometimes it can outperform
the prediction performance of each individual model.

Here, we combine 3 learners (linear and non-linear) and use a ridge regressor
to combine their outputs together.

.. note::
   Although we will make new pipelines with the processors which we wrote in
   the previous section for the 3 learners, the final estimator
   :class:`~sklearn.linear_model.RidgeCV()` does not need preprocessing of
   the data as it will be fed with the already preprocessed output from the 3
   learners.

.. GENERATED FROM PYTHON SOURCE LINES 164-170

.. code-block:: Python


    from sklearn.linear_model import LassoCV

    lasso_pipeline = make_pipeline(linear_preprocessor, LassoCV())
    lasso_pipeline






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    }

    /* On hover, darken the color of the background */
    #sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-23 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-23 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-23 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-23 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-23 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-23 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-23 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-3) 1pt solid;
      color: var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3) 1pt solid;
      color: var(--sklearn-color-fitted-level-3);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-unfitted-level-0);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-fitted-level-0);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-23 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-23 a.estimator_doc_link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-23 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-23 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }

    .estimator-table {
        font-family: monospace;
    }

    .estimator-table summary {
        padding: .5rem;
        cursor: pointer;
    }

    .estimator-table summary::marker {
        font-size: 0.7rem;
    }

    .estimator-table details[open] {
        padding-left: 0.1rem;
        padding-right: 0.1rem;
        padding-bottom: 0.3rem;
    }

    .estimator-table .parameters-table {
        margin-left: auto !important;
        margin-right: auto !important;
        margin-top: 0;
    }

    .estimator-table .parameters-table tr:nth-child(odd) {
        background-color: #fff;
    }

    .estimator-table .parameters-table tr:nth-child(even) {
        background-color: #f6f6f6;
    }

    .estimator-table .parameters-table tr:hover {
        background-color: #e0e0e0;
    }

    .estimator-table table td {
        border: 1px solid rgba(106, 105, 104, 0.232);
    }

    /*
        `table td`is set in notebook with right text-align.
        We need to overwrite it.
    */
    .estimator-table table td.param {
        text-align: left;
        position: relative;
        padding: 0;
    }

    .user-set td {
        color:rgb(255, 94, 0);
        text-align: left !important;
    }

    .user-set td.value {
        color:rgb(255, 94, 0);
        background-color: transparent;
    }

    .default td {
        color: black;
        text-align: left !important;
    }

    .user-set td i,
    .default td i {
        color: black;
    }

    /*
        Styles for parameter documentation links
        We need styling for visited so jupyter doesn't overwrite it
    */
    a.param-doc-link,
    a.param-doc-link:link,
    a.param-doc-link:visited {
        text-decoration: underline dashed;
        text-underline-offset: .3em;
        color: inherit;
        display: block;
        padding: .5em;
    }

    /* "hack" to make the entire area of the cell containing the link clickable */
    a.param-doc-link::before {
        position: absolute;
        content: "";
        inset: 0;
    }

    .param-doc-description {
        display: none;
        position: absolute;
        z-index: 9999;
        left: 0;
        padding: .5ex;
        margin-left: 1.5em;
        color: var(--sklearn-color-text);
        box-shadow: .3em .3em .4em #999;
        width: max-content;
        text-align: left;
        max-height: 10em;
        overflow-y: auto;

        /* unfitted */
        background: var(--sklearn-color-unfitted-level-0);
        border: thin solid var(--sklearn-color-unfitted-level-3);
    }

    /* Fitted state for parameter tooltips */
    .fitted .param-doc-description {
        /* fitted */
        background: var(--sklearn-color-fitted-level-0);
        border: thin solid var(--sklearn-color-fitted-level-3);
    }

    .param-doc-link:hover .param-doc-description {
        display: block;
    }

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-23" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                     ColumnTransformer(transformers=[(&#x27;pipeline&#x27;,
                                                      Pipeline(steps=[(&#x27;standardscaler&#x27;,
                                                                       StandardScaler()),
                                                                      (&#x27;simpleimputer&#x27;,
                                                                       SimpleImputer(add_indicator=True))]),
                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                                     (&#x27;onehotencoder&#x27;,
                                                      OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),
                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;)])),
                    (&#x27;lassocv&#x27;, LassoCV())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-83" type="checkbox" ><label for="sk-estimator-id-83" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content " data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('steps',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples">
                steps
                <span class="param-doc-description">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>
            </a>
        </td>
                <td class="value">[(&#x27;columntransformer&#x27;, ...), (&#x27;lassocv&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transform_input',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone">
                transform_input
                <span class="param-doc-description">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('memory',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone">
                memory
                <span class="param-doc-description">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-84" type="checkbox" ><label for="sk-estimator-id-84" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;pipeline&#x27;, ...), (&#x27;onehotencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-85" type="checkbox" ><label for="sk-estimator-id-85" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__pipeline__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-86" type="checkbox" ><label for="sk-estimator-id-86" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__pipeline__standardscaler__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_mean',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue">
                with_mean
                <span class="param-doc-description">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_std',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue">
                with_std
                <span class="param-doc-description">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-87" type="checkbox" ><label for="sk-estimator-id-87" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__pipeline__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-88" type="checkbox" ><label for="sk-estimator-id-88" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__onehotencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-89" type="checkbox" ><label for="sk-estimator-id-89" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__onehotencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('drop',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone">
                drop
                <span class="param-doc-description">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_output',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue">
                sparse_output
                <span class="param-doc-description">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in "Compressed Sparse Row" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown="infrequent_if_exist"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `"warn"` was added in 1.6.</span>
            </a>
        </td>
                <td class="value">&#x27;ignore&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('feature_name_combiner',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22">
                feature_name_combiner
                <span class="param-doc-description">feature_name_combiner: "concat" or callable, default="concat"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`"concat"` concatenates encoded feature name and category with<br>`feature + "_" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>
            </a>
        </td>
                <td class="value">&#x27;concat&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-90" type="checkbox" ><label for="sk-estimator-id-90" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="lassocv__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('eps',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=eps,-float%2C%20default%3D1e-3">
                eps
                <span class="param-doc-description">eps: float, default=1e-3<br><br>Length of the path. ``eps=1e-3`` means that<br>``alpha_min / alpha_max = 1e-3``.</span>
            </a>
        </td>
                <td class="value">0.001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_alphas',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=n_alphas,-int%2C%20default%3D100">
                n_alphas
                <span class="param-doc-description">n_alphas: int, default=100<br><br>Number of alphas along the regularization path.<br><br>.. deprecated:: 1.7<br>    `n_alphas` was deprecated in 1.7 and will be removed in 1.9. Use `alphas`<br>    instead.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('alphas',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=alphas,-int%2C%20default%3D100">
                alphas
                <span class="param-doc-description">alphas: array-like or int, default=None<br><br>Values of alphas to test along the regularization path.<br>If int, `alphas` values are generated automatically.<br>If array-like, list of alpha values to use.<br><br>.. versionchanged:: 1.7<br>    `alphas` accepts an integer value which removes the need to pass<br>    `n_alphas`.<br><br>.. deprecated:: 1.7<br>    `alphas=None` was deprecated in 1.7 and will be removed in 1.9, at which<br>    point the default value will be set to 100.</span>
            </a>
        </td>
                <td class="value">&#x27;warn&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fit_intercept',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
                fit_intercept
                <span class="param-doc-description">fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to false, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('precompute',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=precompute,-%27auto%27%2C%20bool%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_features%2C%20n_features%29%2C%20default%3D%27auto%27">
                precompute
                <span class="param-doc-description">precompute: 'auto', bool or array-like of shape             (n_features, n_features), default='auto'<br><br>Whether to use a precomputed Gram matrix to speed up<br>calculations. If set to ``'auto'`` let us decide. The Gram<br>matrix can also be passed as argument.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=max_iter,-int%2C%20default%3D1000">
                max_iter
                <span class="param-doc-description">max_iter: int, default=1000<br><br>The maximum number of iterations.</span>
            </a>
        </td>
                <td class="value">1000</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=tol,-float%2C%20default%3D1e-4">
                tol
                <span class="param-doc-description">tol: float, default=1e-4<br><br>The tolerance for the optimization: if the updates are smaller or equal to<br>``tol``, the optimization code checks the dual gap for optimality and continues<br>until it is smaller or equal to ``tol``.</span>
            </a>
        </td>
                <td class="value">0.0001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy_X',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=copy_X,-bool%2C%20default%3DTrue">
                copy_X
                <span class="param-doc-description">copy_X: bool, default=True<br><br>If ``True``, X will be copied; else, it may be overwritten.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('cv',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20iterable%2C%20default%3DNone">
                cv
                <span class="param-doc-description">cv: int, cross-validation generator or iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross-validation,<br>- int, to specify the number of folds.<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For int/None inputs, :class:`~sklearn.model_selection.KFold` is used.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=verbose,-bool%20or%20int%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool or int, default=False<br><br>Amount of verbosity.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of CPUs to use during the cross validation.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('positive',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=positive,-bool%2C%20default%3DFalse">
                positive
                <span class="param-doc-description">positive: bool, default=False<br><br>If positive, restrict regression coefficients to be positive.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance, default=None<br><br>The seed of the pseudo random number generator that selects a random<br>feature to update. Used when ``selection`` == 'random'.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('selection',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=selection,-%7B%27cyclic%27%2C%20%27random%27%7D%2C%20default%3D%27cyclic%27">
                selection
                <span class="param-doc-description">selection: {'cyclic', 'random'}, default='cyclic'<br><br>If set to 'random', a random coefficient is updated every iteration<br>rather than looping over features sequentially by default. This<br>(setting to 'random') often leads to significantly faster convergence<br>especially when tol is higher than 1e-4.</span>
            </a>
        </td>
                <td class="value">&#x27;cyclic&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
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    function forceTheme(elementId) {
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            estimatorElement.classList.add(theme);
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    forceTheme('sk-container-id-23');</script></body>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 171-176

.. code-block:: Python

    from sklearn.ensemble import RandomForestRegressor

    rf_pipeline = make_pipeline(tree_preprocessor, RandomForestRegressor(random_state=42))
    rf_pipeline






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-24 {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: #000;
      --sklearn-color-text-muted: #666;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;
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    #sk-container-id-24.light {
      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: black;
      --sklearn-color-background: white;
      --sklearn-color-border-box: black;
      --sklearn-color-icon: #696969;
    }

    #sk-container-id-24.dark {
      --sklearn-color-text-on-default-background: white;
      --sklearn-color-background: #111;
      --sklearn-color-border-box: white;
      --sklearn-color-icon: #878787;
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    #sk-container-id-24 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-24 pre {
      padding: 0;
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    #sk-container-id-24 input.sk-hidden--visually {
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      clip: rect(1px 1px 1px 1px);
      clip: rect(1px, 1px, 1px, 1px);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
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    #sk-container-id-24 div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
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    #sk-container-id-24 div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-24 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
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    /* Parallel-specific style estimator block */

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      width: 50%;
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    #sk-container-id-24 div.sk-parallel-item:only-child::after {
      width: 0;
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    /* Serial-specific style estimator block */

    #sk-container-id-24 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
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    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-24 div.sk-toggleable {
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    /* Toggleable label */
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      cursor: pointer;
      display: flex;
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      box-sizing: border-box;
      text-align: center;
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      justify-content: center;
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      font-weight: lighter;
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    #sk-container-id-24 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
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      /* unfitted */
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      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-24 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-24 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-24 div.sk-label label.sk-toggleable__label,
    #sk-container-id-24 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-24 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-24 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-24 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-24 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-24 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-24 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-24 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-3) 1pt solid;
      color: var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3) 1pt solid;
      color: var(--sklearn-color-fitted-level-3);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-unfitted-level-0);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-fitted-level-0);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-24 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-24 a.estimator_doc_link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-24 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-24 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }

    .estimator-table {
        font-family: monospace;
    }

    .estimator-table summary {
        padding: .5rem;
        cursor: pointer;
    }

    .estimator-table summary::marker {
        font-size: 0.7rem;
    }

    .estimator-table details[open] {
        padding-left: 0.1rem;
        padding-right: 0.1rem;
        padding-bottom: 0.3rem;
    }

    .estimator-table .parameters-table {
        margin-left: auto !important;
        margin-right: auto !important;
        margin-top: 0;
    }

    .estimator-table .parameters-table tr:nth-child(odd) {
        background-color: #fff;
    }

    .estimator-table .parameters-table tr:nth-child(even) {
        background-color: #f6f6f6;
    }

    .estimator-table .parameters-table tr:hover {
        background-color: #e0e0e0;
    }

    .estimator-table table td {
        border: 1px solid rgba(106, 105, 104, 0.232);
    }

    /*
        `table td`is set in notebook with right text-align.
        We need to overwrite it.
    */
    .estimator-table table td.param {
        text-align: left;
        position: relative;
        padding: 0;
    }

    .user-set td {
        color:rgb(255, 94, 0);
        text-align: left !important;
    }

    .user-set td.value {
        color:rgb(255, 94, 0);
        background-color: transparent;
    }

    .default td {
        color: black;
        text-align: left !important;
    }

    .user-set td i,
    .default td i {
        color: black;
    }

    /*
        Styles for parameter documentation links
        We need styling for visited so jupyter doesn't overwrite it
    */
    a.param-doc-link,
    a.param-doc-link:link,
    a.param-doc-link:visited {
        text-decoration: underline dashed;
        text-underline-offset: .3em;
        color: inherit;
        display: block;
        padding: .5em;
    }

    /* "hack" to make the entire area of the cell containing the link clickable */
    a.param-doc-link::before {
        position: absolute;
        content: "";
        inset: 0;
    }

    .param-doc-description {
        display: none;
        position: absolute;
        z-index: 9999;
        left: 0;
        padding: .5ex;
        margin-left: 1.5em;
        color: var(--sklearn-color-text);
        box-shadow: .3em .3em .4em #999;
        width: max-content;
        text-align: left;
        max-height: 10em;
        overflow-y: auto;

        /* unfitted */
        background: var(--sklearn-color-unfitted-level-0);
        border: thin solid var(--sklearn-color-unfitted-level-3);
    }

    /* Fitted state for parameter tooltips */
    .fitted .param-doc-description {
        /* fitted */
        background: var(--sklearn-color-fitted-level-0);
        border: thin solid var(--sklearn-color-fitted-level-3);
    }

    .param-doc-link:hover .param-doc-description {
        display: block;
    }

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-24" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                     ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                      SimpleImputer(add_indicator=True),
                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                                     (&#x27;ordinalencoder&#x27;,
                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                     unknown_value=-1),
                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;)])),
                    (&#x27;randomforestregressor&#x27;,
                     RandomForestRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-91" type="checkbox" ><label for="sk-estimator-id-91" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content " data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('steps',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples">
                steps
                <span class="param-doc-description">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>
            </a>
        </td>
                <td class="value">[(&#x27;columntransformer&#x27;, ...), (&#x27;randomforestregressor&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transform_input',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone">
                transform_input
                <span class="param-doc-description">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('memory',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone">
                memory
                <span class="param-doc-description">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-92" type="checkbox" ><label for="sk-estimator-id-92" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;simpleimputer&#x27;, ...), (&#x27;ordinalencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-93" type="checkbox" ><label for="sk-estimator-id-93" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__simpleimputer__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-94" type="checkbox" ><label for="sk-estimator-id-94" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-95" type="checkbox" ><label for="sk-estimator-id-95" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__ordinalencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-96" type="checkbox" ><label for="sk-estimator-id-96" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__ordinalencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values, and should be sorted in case of numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27use_encoded_value%27%7D%2C%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'use_encoded_value'}, default='error'<br><br>When set to 'error' an error will be raised in case an unknown<br>categorical feature is present during transform. When set to<br>'use_encoded_value', the encoded value of unknown categories will be<br>set to the value given for the parameter `unknown_value`. In<br>:meth:`inverse_transform`, an unknown category will be denoted as None.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">&#x27;use_encoded_value&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('unknown_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=unknown_value,-int%20or%20np.nan%2C%20default%3DNone">
                unknown_value
                <span class="param-doc-description">unknown_value: int or np.nan, default=None<br><br>When the parameter handle_unknown is set to 'use_encoded_value', this<br>parameter is required and will set the encoded value of unknown<br>categories. It has to be distinct from the values used to encode any of<br>the categories in `fit`. If set to np.nan, the `dtype` parameter must<br>be a float dtype.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('encoded_missing_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=encoded_missing_value,-int%20or%20np.nan%2C%20default%3Dnp.nan">
                encoded_missing_value
                <span class="param-doc-description">encoded_missing_value: int or np.nan, default=np.nan<br><br>Encoded value of missing categories. If set to `np.nan`, then the `dtype`<br>parameter must be a float dtype.<br><br>.. versionadded:: 1.1</span>
            </a>
        </td>
                <td class="value">-2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output categories for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>`max_categories` do **not** take into account missing or unknown<br>categories. Setting `unknown_value` or `encoded_missing_value` to an<br>integer will increase the number of unique integer codes by one each.<br>This can result in up to `max_categories + 2` integer codes.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="randomforestregressor__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_estimators',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=n_estimators,-int%2C%20default%3D100">
                n_estimators
                <span class="param-doc-description">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>
            </a>
        </td>
                <td class="value">100</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('criterion',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=criterion,-%7B%22squared_error%22%2C%20%22absolute_error%22%2C%20%22friedman_mse%22%2C%20%22poisson%22%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%22squared_error%22">
                criterion
                <span class="param-doc-description">criterion: {"squared_error", "absolute_error", "friedman_mse", "poisson"},             default="squared_error"<br><br>The function to measure the quality of a split. Supported criteria<br>are "squared_error" for the mean squared error, which is equal to<br>variance reduction as feature selection criterion and minimizes the L2<br>loss using the mean of each terminal node, "friedman_mse", which uses<br>mean squared error with Friedman's improvement score for potential<br>splits, "absolute_error" for the mean absolute error, which minimizes<br>the L1 loss using the median of each terminal node, and "poisson" which<br>uses reduction in Poisson deviance to find splits.<br>Training using "absolute_error" is significantly slower<br>than when using "squared_error".<br><br>.. versionadded:: 0.18<br>   Mean Absolute Error (MAE) criterion.<br><br>.. versionadded:: 1.0<br>   Poisson criterion.</span>
            </a>
        </td>
                <td class="value">&#x27;squared_error&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_depth',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_depth,-int%2C%20default%3DNone">
                max_depth
                <span class="param-doc-description">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_samples_split',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2">
                min_samples_split
                <span class="param-doc-description">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>
            </a>
        </td>
                <td class="value">2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_samples_leaf',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1">
                min_samples_leaf
                <span class="param-doc-description">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>
            </a>
        </td>
                <td class="value">1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_weight_fraction_leaf',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0">
                min_weight_fraction_leaf
                <span class="param-doc-description">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D1.0">
                max_features
                <span class="param-doc-description">max_features: {"sqrt", "log2", None}, int or float, default=1.0<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If "sqrt", then `max_features=sqrt(n_features)`.<br>- If "log2", then `max_features=log2(n_features)`.<br>- If None or 1.0, then `max_features=n_features`.<br><br>.. note::<br>    The default of 1.0 is equivalent to bagged trees and more<br>    randomness can be achieved by setting smaller values, e.g. 0.3.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `"auto"` to 1.0.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>
            </a>
        </td>
                <td class="value">1.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_leaf_nodes',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone">
                max_leaf_nodes
                <span class="param-doc-description">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_impurity_decrease',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0">
                min_impurity_decrease
                <span class="param-doc-description">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('bootstrap',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=bootstrap,-bool%2C%20default%3DTrue">
                bootstrap
                <span class="param-doc-description">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('oob_score',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse">
                oob_score
                <span class="param-doc-description">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.r2_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>
            </a>
        </td>
                <td class="value">42</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('warm_start',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
                warm_start
                <span class="param-doc-description">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('ccp_alpha',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0">
                ccp_alpha
                <span class="param-doc-description">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_samples',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone">
                max_samples
                <span class="param-doc-description">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('monotonic_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone">
                monotonic_cst
                <span class="param-doc-description">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonically increasing<br>  - 0: no constraint<br>  - -1: monotonically decreasing<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multioutput regressions (i.e. when `n_outputs_ > 1`),<br>  - regressions trained on data with missing values.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 177-184

.. code-block:: Python

    from sklearn.ensemble import HistGradientBoostingRegressor

    gbdt_pipeline = make_pipeline(
        tree_preprocessor, HistGradientBoostingRegressor(random_state=0)
    )
    gbdt_pipeline






.. raw:: html

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      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
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      --sklearn-color-fitted-level-2: #b3dbfd;
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      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-25 label.sk-toggleable__label {
      cursor: pointer;
      display: flex;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
      align-items: center;
      justify-content: center;
      gap: 0.5em;
    }

    #sk-container-id-25 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-25 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-25 div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-25 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-25 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-25 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-25 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-25 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-25 div.sk-label label.sk-toggleable__label,
    #sk-container-id-25 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-25 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-25 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-25 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-25 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-25 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-25 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-25 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-3) 1pt solid;
      color: var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3) 1pt solid;
      color: var(--sklearn-color-fitted-level-3);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-unfitted-level-0);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-fitted-level-0);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-25 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-25 a.estimator_doc_link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-25 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-25 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }

    .estimator-table {
        font-family: monospace;
    }

    .estimator-table summary {
        padding: .5rem;
        cursor: pointer;
    }

    .estimator-table summary::marker {
        font-size: 0.7rem;
    }

    .estimator-table details[open] {
        padding-left: 0.1rem;
        padding-right: 0.1rem;
        padding-bottom: 0.3rem;
    }

    .estimator-table .parameters-table {
        margin-left: auto !important;
        margin-right: auto !important;
        margin-top: 0;
    }

    .estimator-table .parameters-table tr:nth-child(odd) {
        background-color: #fff;
    }

    .estimator-table .parameters-table tr:nth-child(even) {
        background-color: #f6f6f6;
    }

    .estimator-table .parameters-table tr:hover {
        background-color: #e0e0e0;
    }

    .estimator-table table td {
        border: 1px solid rgba(106, 105, 104, 0.232);
    }

    /*
        `table td`is set in notebook with right text-align.
        We need to overwrite it.
    */
    .estimator-table table td.param {
        text-align: left;
        position: relative;
        padding: 0;
    }

    .user-set td {
        color:rgb(255, 94, 0);
        text-align: left !important;
    }

    .user-set td.value {
        color:rgb(255, 94, 0);
        background-color: transparent;
    }

    .default td {
        color: black;
        text-align: left !important;
    }

    .user-set td i,
    .default td i {
        color: black;
    }

    /*
        Styles for parameter documentation links
        We need styling for visited so jupyter doesn't overwrite it
    */
    a.param-doc-link,
    a.param-doc-link:link,
    a.param-doc-link:visited {
        text-decoration: underline dashed;
        text-underline-offset: .3em;
        color: inherit;
        display: block;
        padding: .5em;
    }

    /* "hack" to make the entire area of the cell containing the link clickable */
    a.param-doc-link::before {
        position: absolute;
        content: "";
        inset: 0;
    }

    .param-doc-description {
        display: none;
        position: absolute;
        z-index: 9999;
        left: 0;
        padding: .5ex;
        margin-left: 1.5em;
        color: var(--sklearn-color-text);
        box-shadow: .3em .3em .4em #999;
        width: max-content;
        text-align: left;
        max-height: 10em;
        overflow-y: auto;

        /* unfitted */
        background: var(--sklearn-color-unfitted-level-0);
        border: thin solid var(--sklearn-color-unfitted-level-3);
    }

    /* Fitted state for parameter tooltips */
    .fitted .param-doc-description {
        /* fitted */
        background: var(--sklearn-color-fitted-level-0);
        border: thin solid var(--sklearn-color-fitted-level-3);
    }

    .param-doc-link:hover .param-doc-description {
        display: block;
    }

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-25" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,
                     ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                      SimpleImputer(add_indicator=True),
                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                                     (&#x27;ordinalencoder&#x27;,
                                                      OrdinalEncoder(encoded_missing_value=-2,
                                                                     handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                     unknown_value=-1),
                                                      &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;)])),
                    (&#x27;histgradientboostingregressor&#x27;,
                     HistGradientBoostingRegressor(random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-98" type="checkbox" ><label for="sk-estimator-id-98" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content " data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('steps',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples">
                steps
                <span class="param-doc-description">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>
            </a>
        </td>
                <td class="value">[(&#x27;columntransformer&#x27;, ...), (&#x27;histgradientboostingregressor&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transform_input',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone">
                transform_input
                <span class="param-doc-description">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('memory',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone">
                memory
                <span class="param-doc-description">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-99" type="checkbox" ><label for="sk-estimator-id-99" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;simpleimputer&#x27;, ...), (&#x27;ordinalencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-100" type="checkbox" ><label for="sk-estimator-id-100" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__simpleimputer__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-101" type="checkbox" ><label for="sk-estimator-id-101" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-102" type="checkbox" ><label for="sk-estimator-id-102" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__ordinalencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-103" type="checkbox" ><label for="sk-estimator-id-103" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="columntransformer__ordinalencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values, and should be sorted in case of numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27use_encoded_value%27%7D%2C%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'use_encoded_value'}, default='error'<br><br>When set to 'error' an error will be raised in case an unknown<br>categorical feature is present during transform. When set to<br>'use_encoded_value', the encoded value of unknown categories will be<br>set to the value given for the parameter `unknown_value`. In<br>:meth:`inverse_transform`, an unknown category will be denoted as None.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">&#x27;use_encoded_value&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('unknown_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=unknown_value,-int%20or%20np.nan%2C%20default%3DNone">
                unknown_value
                <span class="param-doc-description">unknown_value: int or np.nan, default=None<br><br>When the parameter handle_unknown is set to 'use_encoded_value', this<br>parameter is required and will set the encoded value of unknown<br>categories. It has to be distinct from the values used to encode any of<br>the categories in `fit`. If set to np.nan, the `dtype` parameter must<br>be a float dtype.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('encoded_missing_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=encoded_missing_value,-int%20or%20np.nan%2C%20default%3Dnp.nan">
                encoded_missing_value
                <span class="param-doc-description">encoded_missing_value: int or np.nan, default=np.nan<br><br>Encoded value of missing categories. If set to `np.nan`, then the `dtype`<br>parameter must be a float dtype.<br><br>.. versionadded:: 1.1</span>
            </a>
        </td>
                <td class="value">-2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output categories for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>`max_categories` do **not** take into account missing or unknown<br>categories. Setting `unknown_value` or `encoded_missing_value` to an<br>integer will increase the number of unique integer codes by one each.<br>This can result in up to `max_categories + 2` integer codes.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-104" type="checkbox" ><label for="sk-estimator-id-104" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="histgradientboostingregressor__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('loss',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=loss,-%7B%27squared_error%27%2C%20%27absolute_error%27%2C%20%27gamma%27%2C%20%27poisson%27%2C%20%27quantile%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27squared_error%27">
                loss
                <span class="param-doc-description">loss: {'squared_error', 'absolute_error', 'gamma', 'poisson', 'quantile'},             default='squared_error'<br><br>The loss function to use in the boosting process. Note that the<br>"squared error", "gamma" and "poisson" losses actually implement<br>"half least squares loss", "half gamma deviance" and "half poisson<br>deviance" to simplify the computation of the gradient. Furthermore,<br>"gamma" and "poisson" losses internally use a log-link, "gamma"<br>requires ``y > 0`` and "poisson" requires ``y >= 0``.<br>"quantile" uses the pinball loss.<br><br>.. versionchanged:: 0.23<br>   Added option 'poisson'.<br><br>.. versionchanged:: 1.1<br>   Added option 'quantile'.<br><br>.. versionchanged:: 1.3<br>   Added option 'gamma'.</span>
            </a>
        </td>
                <td class="value">&#x27;squared_error&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('quantile',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=quantile,-float%2C%20default%3DNone">
                quantile
                <span class="param-doc-description">quantile: float, default=None<br><br>If loss is "quantile", this parameter specifies which quantile to be estimated<br>and must be between 0 and 1.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('learning_rate',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=learning_rate,-float%2C%20default%3D0.1">
                learning_rate
                <span class="param-doc-description">learning_rate: float, default=0.1<br><br>The learning rate, also known as *shrinkage*. This is used as a<br>multiplicative factor for the leaves values. Use ``1`` for no<br>shrinkage.</span>
            </a>
        </td>
                <td class="value">0.1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_iter,-int%2C%20default%3D100">
                max_iter
                <span class="param-doc-description">max_iter: int, default=100<br><br>The maximum number of iterations of the boosting process, i.e. the<br>maximum number of trees.</span>
            </a>
        </td>
                <td class="value">100</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_leaf_nodes',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_leaf_nodes,-int%20or%20None%2C%20default%3D31">
                max_leaf_nodes
                <span class="param-doc-description">max_leaf_nodes: int or None, default=31<br><br>The maximum number of leaves for each tree. Must be strictly greater<br>than 1. If None, there is no maximum limit.</span>
            </a>
        </td>
                <td class="value">31</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_depth',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_depth,-int%20or%20None%2C%20default%3DNone">
                max_depth
                <span class="param-doc-description">max_depth: int or None, default=None<br><br>The maximum depth of each tree. The depth of a tree is the number of<br>edges to go from the root to the deepest leaf.<br>Depth isn't constrained by default.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_samples_leaf',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=min_samples_leaf,-int%2C%20default%3D20">
                min_samples_leaf
                <span class="param-doc-description">min_samples_leaf: int, default=20<br><br>The minimum number of samples per leaf. For small datasets with less<br>than a few hundred samples, it is recommended to lower this value<br>since only very shallow trees would be built.</span>
            </a>
        </td>
                <td class="value">20</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('l2_regularization',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=l2_regularization,-float%2C%20default%3D0">
                l2_regularization
                <span class="param-doc-description">l2_regularization: float, default=0<br><br>The L2 regularization parameter penalizing leaves with small hessians.<br>Use ``0`` for no regularization (default).</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_features,-float%2C%20default%3D1.0">
                max_features
                <span class="param-doc-description">max_features: float, default=1.0<br><br>Proportion of randomly chosen features in each and every node split.<br>This is a form of regularization, smaller values make the trees weaker<br>learners and might prevent overfitting.<br>If interaction constraints from `interaction_cst` are present, only allowed<br>features are taken into account for the subsampling.<br><br>.. versionadded:: 1.4</span>
            </a>
        </td>
                <td class="value">1.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_bins',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_bins,-int%2C%20default%3D255">
                max_bins
                <span class="param-doc-description">max_bins: int, default=255<br><br>The maximum number of bins to use for non-missing values. Before<br>training, each feature of the input array `X` is binned into<br>integer-valued bins, which allows for a much faster training stage.<br>Features with a small number of unique values may use less than<br>``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin<br>is always reserved for missing values. Must be no larger than 255.</span>
            </a>
        </td>
                <td class="value">255</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categorical_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=categorical_features,-array-like%20of%20%7Bbool%2C%20int%2C%20str%7D%20of%20shape%20%28n_features%29%20%20%20%20%20%20%20%20%20%20%20%20%20or%20shape%20%28n_categorical_features%2C%29%2C%20default%3D%27from_dtype%27">
                categorical_features
                <span class="param-doc-description">categorical_features: array-like of {bool, int, str} of shape (n_features)             or shape (n_categorical_features,), default='from_dtype'<br><br>Indicates the categorical features.<br><br>- None : no feature will be considered categorical.<br>- boolean array-like : boolean mask indicating categorical features.<br>- integer array-like : integer indices indicating categorical<br>  features.<br>- str array-like: names of categorical features (assuming the training<br>  data has feature names).<br>- `"from_dtype"`: dataframe columns with dtype "category" are<br>  considered to be categorical features. The input must be an object<br>  exposing a ``__dataframe__`` method such as pandas or polars<br>  DataFrames to use this feature.<br><br>For each categorical feature, there must be at most `max_bins` unique<br>categories. Negative values for categorical features encoded as numeric<br>dtypes are treated as missing values. All categorical values are<br>converted to floating point numbers. This means that categorical values<br>of 1.0 and 1 are treated as the same category.<br><br>Read more in the :ref:`User Guide <categorical_support_gbdt>` and<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`.<br><br>.. versionadded:: 0.24<br><br>.. versionchanged:: 1.2<br>   Added support for feature names.<br><br>.. versionchanged:: 1.4<br>   Added `"from_dtype"` option.<br><br>.. versionchanged:: 1.6<br>   The default value changed from `None` to `"from_dtype"`.</span>
            </a>
        </td>
                <td class="value">&#x27;from_dtype&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('monotonic_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%20or%20dict%2C%20default%3DNone">
                monotonic_cst
                <span class="param-doc-description">monotonic_cst: array-like of int of shape (n_features) or dict, default=None<br><br>Monotonic constraint to enforce on each feature are specified using the<br>following integer values:<br><br>- 1: monotonic increase<br>- 0: no constraint<br>- -1: monotonic decrease<br><br>If a dict with str keys, map feature to monotonic constraints by name.<br>If an array, the features are mapped to constraints by position. See<br>:ref:`monotonic_cst_features_names` for a usage example.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 0.23<br><br>.. versionchanged:: 1.2<br>   Accept dict of constraints with feature names as keys.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('interaction_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=interaction_cst,-%7B%22pairwise%22%2C%20%22no_interactions%22%7D%20or%20sequence%20of%20lists/tuples/sets%20%20%20%20%20%20%20%20%20%20%20%20%20of%20int%2C%20default%3DNone">
                interaction_cst
                <span class="param-doc-description">interaction_cst: {"pairwise", "no_interactions"} or sequence of lists/tuples/sets             of int, default=None<br><br>Specify interaction constraints, the sets of features which can<br>interact with each other in child node splits.<br><br>Each item specifies the set of feature indices that are allowed<br>to interact with each other. If there are more features than<br>specified in these constraints, they are treated as if they were<br>specified as an additional set.<br><br>The strings "pairwise" and "no_interactions" are shorthands for<br>allowing only pairwise or no interactions, respectively.<br><br>For instance, with 5 features in total, `interaction_cst=[{0, 1}]`<br>is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`,<br>and specifies that each branch of a tree will either only split<br>on features 0 and 1 or only split on features 2, 3 and 4.<br><br>See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('warm_start',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
                warm_start
                <span class="param-doc-description">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble. For results to be valid, the<br>estimator should be re-trained on the same data only.<br>See :term:`the Glossary <warm_start>`.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('early_stopping',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=early_stopping,-%27auto%27%20or%20bool%2C%20default%3D%27auto%27">
                early_stopping
                <span class="param-doc-description">early_stopping: 'auto' or bool, default='auto'<br><br>If 'auto', early stopping is enabled if the sample size is larger than<br>10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping<br>is enabled, otherwise early stopping is disabled.<br><br>.. versionadded:: 0.23</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('scoring',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=scoring,-str%20or%20callable%20or%20None%2C%20default%3D%27loss%27">
                scoring
                <span class="param-doc-description">scoring: str or callable or None, default='loss'<br><br>Scoring method to use for early stopping. Only used if `early_stopping`<br>is enabled. Options:<br><br>- str: see :ref:`scoring_string_names` for options.<br>- callable: a scorer callable object (e.g., function) with signature<br>  ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details.<br>- `None`: the :ref:`coefficient of determination <r2_score>`<br>  (:math:`R^2`) is used.<br>- 'loss': early stopping is checked w.r.t the loss value.</span>
            </a>
        </td>
                <td class="value">&#x27;loss&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('validation_fraction',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=validation_fraction,-int%20or%20float%20or%20None%2C%20default%3D0.1">
                validation_fraction
                <span class="param-doc-description">validation_fraction: int or float or None, default=0.1<br><br>Proportion (or absolute size) of training data to set aside as<br>validation data for early stopping. If None, early stopping is done on<br>the training data.<br>The value is ignored if either early stopping is not performed, e.g.<br>`early_stopping=False`, or if `X_val` and `y_val` are passed to fit.</span>
            </a>
        </td>
                <td class="value">0.1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_iter_no_change',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=n_iter_no_change,-int%2C%20default%3D10">
                n_iter_no_change
                <span class="param-doc-description">n_iter_no_change: int, default=10<br><br>Used to determine when to "early stop". The fitting process is<br>stopped when none of the last ``n_iter_no_change`` scores are better<br>than the ``n_iter_no_change - 1`` -th-to-last one, up to some<br>tolerance. Only used if early stopping is performed.</span>
            </a>
        </td>
                <td class="value">10</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=tol,-float%2C%20default%3D1e-7">
                tol
                <span class="param-doc-description">tol: float, default=1e-7<br><br>The absolute tolerance to use when comparing scores during early<br>stopping. The higher the tolerance, the more likely we are to early<br>stop: higher tolerance means that it will be harder for subsequent<br>iterations to be considered an improvement upon the reference score.</span>
            </a>
        </td>
                <td class="value">1e-07</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>The verbosity level. If not zero, print some information about the<br>fitting process. ``1`` prints only summary info, ``2`` prints info per<br>iteration.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance or None, default=None<br><br>Pseudo-random number generator to control the subsampling in the<br>binning process, and the train/validation data split if early stopping<br>is enabled.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
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.. GENERATED FROM PYTHON SOURCE LINES 185-197

.. code-block:: Python

    from sklearn.ensemble import StackingRegressor
    from sklearn.linear_model import RidgeCV

    estimators = [
        ("Random Forest", rf_pipeline),
        ("Lasso", lasso_pipeline),
        ("Gradient Boosting", gbdt_pipeline),
    ]

    stacking_regressor = StackingRegressor(estimators=estimators, final_estimator=RidgeCV())
    stacking_regressor






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-26 {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: #000;
      --sklearn-color-text-muted: #666;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;
    }

    #sk-container-id-26.light {
      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: black;
      --sklearn-color-background: white;
      --sklearn-color-border-box: black;
      --sklearn-color-icon: #696969;
    }

    #sk-container-id-26.dark {
      --sklearn-color-text-on-default-background: white;
      --sklearn-color-background: #111;
      --sklearn-color-border-box: white;
      --sklearn-color-icon: #878787;
    }

    #sk-container-id-26 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-26 pre {
      padding: 0;
    }

    #sk-container-id-26 input.sk-hidden--visually {
      border: 0;
      clip: rect(1px 1px 1px 1px);
      clip: rect(1px, 1px, 1px, 1px);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
    }

    #sk-container-id-26 div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
      padding-bottom: 0.4em;
      background-color: var(--sklearn-color-background);
    }

    #sk-container-id-26 div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-26 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    #sk-container-id-26 div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    #sk-container-id-26 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-26 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-26 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-26 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-26 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-26 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    clickable and can be expanded/collapsed.
    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-26 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-26 label.sk-toggleable__label {
      cursor: pointer;
      display: flex;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
      align-items: center;
      justify-content: center;
      gap: 0.5em;
    }

    #sk-container-id-26 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-26 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-26 div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-26 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-26 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-26 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-26 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-26 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-26 div.sk-label label.sk-toggleable__label,
    #sk-container-id-26 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-26 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-26 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-26 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-26 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-26 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-26 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-26 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-3) 1pt solid;
      color: var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3) 1pt solid;
      color: var(--sklearn-color-fitted-level-3);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-unfitted-level-0);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      border: var(--sklearn-color-fitted-level-0) 1pt solid;
      color: var(--sklearn-color-fitted-level-0);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-26 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-unfitted-level-0);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-26 a.estimator_doc_link.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-26 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-26 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }

    .estimator-table {
        font-family: monospace;
    }

    .estimator-table summary {
        padding: .5rem;
        cursor: pointer;
    }

    .estimator-table summary::marker {
        font-size: 0.7rem;
    }

    .estimator-table details[open] {
        padding-left: 0.1rem;
        padding-right: 0.1rem;
        padding-bottom: 0.3rem;
    }

    .estimator-table .parameters-table {
        margin-left: auto !important;
        margin-right: auto !important;
        margin-top: 0;
    }

    .estimator-table .parameters-table tr:nth-child(odd) {
        background-color: #fff;
    }

    .estimator-table .parameters-table tr:nth-child(even) {
        background-color: #f6f6f6;
    }

    .estimator-table .parameters-table tr:hover {
        background-color: #e0e0e0;
    }

    .estimator-table table td {
        border: 1px solid rgba(106, 105, 104, 0.232);
    }

    /*
        `table td`is set in notebook with right text-align.
        We need to overwrite it.
    */
    .estimator-table table td.param {
        text-align: left;
        position: relative;
        padding: 0;
    }

    .user-set td {
        color:rgb(255, 94, 0);
        text-align: left !important;
    }

    .user-set td.value {
        color:rgb(255, 94, 0);
        background-color: transparent;
    }

    .default td {
        color: black;
        text-align: left !important;
    }

    .user-set td i,
    .default td i {
        color: black;
    }

    /*
        Styles for parameter documentation links
        We need styling for visited so jupyter doesn't overwrite it
    */
    a.param-doc-link,
    a.param-doc-link:link,
    a.param-doc-link:visited {
        text-decoration: underline dashed;
        text-underline-offset: .3em;
        color: inherit;
        display: block;
        padding: .5em;
    }

    /* "hack" to make the entire area of the cell containing the link clickable */
    a.param-doc-link::before {
        position: absolute;
        content: "";
        inset: 0;
    }

    .param-doc-description {
        display: none;
        position: absolute;
        z-index: 9999;
        left: 0;
        padding: .5ex;
        margin-left: 1.5em;
        color: var(--sklearn-color-text);
        box-shadow: .3em .3em .4em #999;
        width: max-content;
        text-align: left;
        max-height: 10em;
        overflow-y: auto;

        /* unfitted */
        background: var(--sklearn-color-unfitted-level-0);
        border: thin solid var(--sklearn-color-unfitted-level-3);
    }

    /* Fitted state for parameter tooltips */
    .fitted .param-doc-description {
        /* fitted */
        background: var(--sklearn-color-fitted-level-0);
        border: thin solid var(--sklearn-color-fitted-level-3);
    }

    .param-doc-link:hover .param-doc-description {
        display: block;
    }

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-26" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>StackingRegressor(estimators=[(&#x27;Random Forest&#x27;,
                                   Pipeline(steps=[(&#x27;columntransformer&#x27;,
                                                    ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,
                                                                                     SimpleImputer(add_indicator=True),
                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                                                                    (&#x27;ordinalencoder&#x27;,
                                                                                     OrdinalEncoder(encoded_missing_value=-2,
                                                                                                    handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                    unknown_v...
                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;),
                                                                                    (&#x27;ordinalencoder&#x27;,
                                                                                     OrdinalEncoder(encoded_missing_value=-2,
                                                                                                    handle_unknown=&#x27;use_encoded_value&#x27;,
                                                                                                    unknown_value=-1),
                                                                                     &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;)])),
                                                   (&#x27;histgradientboostingregressor&#x27;,
                                                    HistGradientBoostingRegressor(random_state=0))]))],
                      final_estimator=RidgeCV())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-105" type="checkbox" ><label for="sk-estimator-id-105" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StackingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html">?<span>Documentation for StackingRegressor</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></div></label><div class="sk-toggleable__content " data-param-prefix="">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('estimators',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29">
                estimators
                <span class="param-doc-description">estimators: list of (str, estimator)<br><br>Base estimators which will be stacked together. Each element of the<br>list is defined as a tuple of string (i.e. name) and an estimator<br>instance. An estimator can be set to 'drop' using `set_params`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;Random Forest&#x27;, ...), (&#x27;Lasso&#x27;, ...), ...]</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('final_estimator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html#:~:text=final_estimator,-estimator%2C%20default%3DNone">
                final_estimator
                <span class="param-doc-description">final_estimator: estimator, default=None<br><br>A regressor which will be used to combine the base estimators.<br>The default regressor is a :class:`~sklearn.linear_model.RidgeCV`.</span>
            </a>
        </td>
                <td class="value">RidgeCV()</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('cv',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html#:~:text=cv,-int%2C%20cross-validation%20generator%2C%20iterable%2C%20or%20%22prefit%22%2C%20default%3DNone">
                cv
                <span class="param-doc-description">cv: int, cross-validation generator, iterable, or "prefit", default=None<br><br>Determines the cross-validation splitting strategy used in<br>`cross_val_predict` to train `final_estimator`. Possible inputs for<br>cv are:<br><br>* None, to use the default 5-fold cross validation,<br>* integer, to specify the number of folds in a (Stratified) KFold,<br>* An object to be used as a cross-validation generator,<br>* An iterable yielding train, test splits,<br>* `"prefit"`, to assume the `estimators` are prefit. In this case, the<br>  estimators will not be refitted.<br><br>For integer/None inputs, if the estimator is a classifier and y is<br>either binary or multiclass,<br>:class:`~sklearn.model_selection.StratifiedKFold` is used.<br>In all other cases, :class:`~sklearn.model_selection.KFold` is used.<br>These splitters are instantiated with `shuffle=False` so the splits<br>will be the same across calls.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>If "prefit" is passed, it is assumed that all `estimators` have<br>been fitted already. The `final_estimator_` is trained on the `estimators`<br>predictions on the full training set and are **not** cross validated<br>predictions. Please note that if the models have been trained on the same<br>data to train the stacking model, there is a very high risk of overfitting.<br><br>.. versionadded:: 1.1<br>    The 'prefit' option was added in 1.1<br><br>.. note::<br>   A larger number of split will provide no benefits if the number<br>   of training samples is large enough. Indeed, the training time<br>   will increase. ``cv`` is not used for model evaluation but for<br>   prediction.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>The number of jobs to run in parallel for `fit` of all `estimators`.<br>`None` means 1 unless in a `joblib.parallel_backend` context. -1 means<br>using all processors. See :term:`Glossary <n_jobs>` for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('passthrough',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html#:~:text=passthrough,-bool%2C%20default%3DFalse">
                passthrough
                <span class="param-doc-description">passthrough: bool, default=False<br><br>When False, only the predictions of estimators will be used as<br>training data for `final_estimator`. When True, the<br>`final_estimator` is trained on the predictions as well as the<br>original training data.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.StackingRegressor.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>Verbosity level.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Random Forest</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-106" type="checkbox" ><label for="sk-estimator-id-106" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Random Forest__columntransformer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;simpleimputer&#x27;, ...), (&#x27;ordinalencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-107" type="checkbox" ><label for="sk-estimator-id-107" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content " data-param-prefix="Random Forest__columntransformer__simpleimputer__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-108" type="checkbox" ><label for="sk-estimator-id-108" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Random Forest__columntransformer__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-109" type="checkbox" ><label for="sk-estimator-id-109" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="Random Forest__columntransformer__ordinalencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-110" type="checkbox" ><label for="sk-estimator-id-110" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Random Forest__columntransformer__ordinalencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values, and should be sorted in case of numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27use_encoded_value%27%7D%2C%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'use_encoded_value'}, default='error'<br><br>When set to 'error' an error will be raised in case an unknown<br>categorical feature is present during transform. When set to<br>'use_encoded_value', the encoded value of unknown categories will be<br>set to the value given for the parameter `unknown_value`. In<br>:meth:`inverse_transform`, an unknown category will be denoted as None.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">&#x27;use_encoded_value&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('unknown_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=unknown_value,-int%20or%20np.nan%2C%20default%3DNone">
                unknown_value
                <span class="param-doc-description">unknown_value: int or np.nan, default=None<br><br>When the parameter handle_unknown is set to 'use_encoded_value', this<br>parameter is required and will set the encoded value of unknown<br>categories. It has to be distinct from the values used to encode any of<br>the categories in `fit`. If set to np.nan, the `dtype` parameter must<br>be a float dtype.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('encoded_missing_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=encoded_missing_value,-int%20or%20np.nan%2C%20default%3Dnp.nan">
                encoded_missing_value
                <span class="param-doc-description">encoded_missing_value: int or np.nan, default=np.nan<br><br>Encoded value of missing categories. If set to `np.nan`, then the `dtype`<br>parameter must be a float dtype.<br><br>.. versionadded:: 1.1</span>
            </a>
        </td>
                <td class="value">-2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output categories for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>`max_categories` do **not** take into account missing or unknown<br>categories. Setting `unknown_value` or `encoded_missing_value` to an<br>integer will increase the number of unique integer codes by one each.<br>This can result in up to `max_categories + 2` integer codes.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-111" type="checkbox" ><label for="sk-estimator-id-111" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Random Forest__randomforestregressor__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_estimators',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=n_estimators,-int%2C%20default%3D100">
                n_estimators
                <span class="param-doc-description">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>
            </a>
        </td>
                <td class="value">100</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('criterion',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=criterion,-%7B%22squared_error%22%2C%20%22absolute_error%22%2C%20%22friedman_mse%22%2C%20%22poisson%22%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%22squared_error%22">
                criterion
                <span class="param-doc-description">criterion: {"squared_error", "absolute_error", "friedman_mse", "poisson"},             default="squared_error"<br><br>The function to measure the quality of a split. Supported criteria<br>are "squared_error" for the mean squared error, which is equal to<br>variance reduction as feature selection criterion and minimizes the L2<br>loss using the mean of each terminal node, "friedman_mse", which uses<br>mean squared error with Friedman's improvement score for potential<br>splits, "absolute_error" for the mean absolute error, which minimizes<br>the L1 loss using the median of each terminal node, and "poisson" which<br>uses reduction in Poisson deviance to find splits.<br>Training using "absolute_error" is significantly slower<br>than when using "squared_error".<br><br>.. versionadded:: 0.18<br>   Mean Absolute Error (MAE) criterion.<br><br>.. versionadded:: 1.0<br>   Poisson criterion.</span>
            </a>
        </td>
                <td class="value">&#x27;squared_error&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_depth',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_depth,-int%2C%20default%3DNone">
                max_depth
                <span class="param-doc-description">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_samples_split',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2">
                min_samples_split
                <span class="param-doc-description">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>
            </a>
        </td>
                <td class="value">2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_samples_leaf',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1">
                min_samples_leaf
                <span class="param-doc-description">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>
            </a>
        </td>
                <td class="value">1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_weight_fraction_leaf',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0">
                min_weight_fraction_leaf
                <span class="param-doc-description">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D1.0">
                max_features
                <span class="param-doc-description">max_features: {"sqrt", "log2", None}, int or float, default=1.0<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If "sqrt", then `max_features=sqrt(n_features)`.<br>- If "log2", then `max_features=log2(n_features)`.<br>- If None or 1.0, then `max_features=n_features`.<br><br>.. note::<br>    The default of 1.0 is equivalent to bagged trees and more<br>    randomness can be achieved by setting smaller values, e.g. 0.3.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `"auto"` to 1.0.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>
            </a>
        </td>
                <td class="value">1.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_leaf_nodes',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone">
                max_leaf_nodes
                <span class="param-doc-description">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_impurity_decrease',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0">
                min_impurity_decrease
                <span class="param-doc-description">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('bootstrap',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=bootstrap,-bool%2C%20default%3DTrue">
                bootstrap
                <span class="param-doc-description">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('oob_score',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse">
                oob_score
                <span class="param-doc-description">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.r2_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>
            </a>
        </td>
                <td class="value">42</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('warm_start',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
                warm_start
                <span class="param-doc-description">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('ccp_alpha',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0">
                ccp_alpha
                <span class="param-doc-description">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_samples',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone">
                max_samples
                <span class="param-doc-description">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('monotonic_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone">
                monotonic_cst
                <span class="param-doc-description">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonically increasing<br>  - 0: no constraint<br>  - -1: monotonically decreasing<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multioutput regressions (i.e. when `n_outputs_ > 1`),<br>  - regressions trained on data with missing values.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Lasso</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-112" type="checkbox" ><label for="sk-estimator-id-112" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__columntransformer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;pipeline&#x27;, ...), (&#x27;onehotencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-113" type="checkbox" ><label for="sk-estimator-id-113" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>pipeline</div></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__columntransformer__pipeline__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-114" type="checkbox" ><label for="sk-estimator-id-114" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__columntransformer__pipeline__standardscaler__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_mean',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue">
                with_mean
                <span class="param-doc-description">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_std',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue">
                with_std
                <span class="param-doc-description">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-115" type="checkbox" ><label for="sk-estimator-id-115" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__columntransformer__pipeline__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-116" type="checkbox" ><label for="sk-estimator-id-116" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>onehotencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__columntransformer__onehotencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-117" type="checkbox" ><label for="sk-estimator-id-117" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__columntransformer__onehotencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('drop',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone">
                drop
                <span class="param-doc-description">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_output',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue">
                sparse_output
                <span class="param-doc-description">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in "Compressed Sparse Row" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown="infrequent_if_exist"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `"warn"` was added in 1.6.</span>
            </a>
        </td>
                <td class="value">&#x27;ignore&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('feature_name_combiner',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22">
                feature_name_combiner
                <span class="param-doc-description">feature_name_combiner: "concat" or callable, default="concat"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`"concat"` concatenates encoded feature name and category with<br>`feature + "_" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>
            </a>
        </td>
                <td class="value">&#x27;concat&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-118" type="checkbox" ><label for="sk-estimator-id-118" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>LassoCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html">?<span>Documentation for LassoCV</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Lasso__lassocv__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('eps',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=eps,-float%2C%20default%3D1e-3">
                eps
                <span class="param-doc-description">eps: float, default=1e-3<br><br>Length of the path. ``eps=1e-3`` means that<br>``alpha_min / alpha_max = 1e-3``.</span>
            </a>
        </td>
                <td class="value">0.001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_alphas',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=n_alphas,-int%2C%20default%3D100">
                n_alphas
                <span class="param-doc-description">n_alphas: int, default=100<br><br>Number of alphas along the regularization path.<br><br>.. deprecated:: 1.7<br>    `n_alphas` was deprecated in 1.7 and will be removed in 1.9. Use `alphas`<br>    instead.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('alphas',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=alphas,-int%2C%20default%3D100">
                alphas
                <span class="param-doc-description">alphas: array-like or int, default=None<br><br>Values of alphas to test along the regularization path.<br>If int, `alphas` values are generated automatically.<br>If array-like, list of alpha values to use.<br><br>.. versionchanged:: 1.7<br>    `alphas` accepts an integer value which removes the need to pass<br>    `n_alphas`.<br><br>.. deprecated:: 1.7<br>    `alphas=None` was deprecated in 1.7 and will be removed in 1.9, at which<br>    point the default value will be set to 100.</span>
            </a>
        </td>
                <td class="value">&#x27;warn&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fit_intercept',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
                fit_intercept
                <span class="param-doc-description">fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to false, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('precompute',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=precompute,-%27auto%27%2C%20bool%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_features%2C%20n_features%29%2C%20default%3D%27auto%27">
                precompute
                <span class="param-doc-description">precompute: 'auto', bool or array-like of shape             (n_features, n_features), default='auto'<br><br>Whether to use a precomputed Gram matrix to speed up<br>calculations. If set to ``'auto'`` let us decide. The Gram<br>matrix can also be passed as argument.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=max_iter,-int%2C%20default%3D1000">
                max_iter
                <span class="param-doc-description">max_iter: int, default=1000<br><br>The maximum number of iterations.</span>
            </a>
        </td>
                <td class="value">1000</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=tol,-float%2C%20default%3D1e-4">
                tol
                <span class="param-doc-description">tol: float, default=1e-4<br><br>The tolerance for the optimization: if the updates are smaller or equal to<br>``tol``, the optimization code checks the dual gap for optimality and continues<br>until it is smaller or equal to ``tol``.</span>
            </a>
        </td>
                <td class="value">0.0001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy_X',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=copy_X,-bool%2C%20default%3DTrue">
                copy_X
                <span class="param-doc-description">copy_X: bool, default=True<br><br>If ``True``, X will be copied; else, it may be overwritten.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('cv',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20iterable%2C%20default%3DNone">
                cv
                <span class="param-doc-description">cv: int, cross-validation generator or iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the default 5-fold cross-validation,<br>- int, to specify the number of folds.<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For int/None inputs, :class:`~sklearn.model_selection.KFold` is used.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.<br><br>.. versionchanged:: 0.22<br>    ``cv`` default value if None changed from 3-fold to 5-fold.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=verbose,-bool%20or%20int%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool or int, default=False<br><br>Amount of verbosity.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of CPUs to use during the cross validation.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('positive',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=positive,-bool%2C%20default%3DFalse">
                positive
                <span class="param-doc-description">positive: bool, default=False<br><br>If positive, restrict regression coefficients to be positive.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance, default=None<br><br>The seed of the pseudo random number generator that selects a random<br>feature to update. Used when ``selection`` == 'random'.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('selection',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LassoCV.html#:~:text=selection,-%7B%27cyclic%27%2C%20%27random%27%7D%2C%20default%3D%27cyclic%27">
                selection
                <span class="param-doc-description">selection: {'cyclic', 'random'}, default='cyclic'<br><br>If set to 'random', a random coefficient is updated every iteration<br>rather than looping over features sequentially by default. This<br>(setting to 'random') often leads to significantly faster convergence<br>especially when tol is higher than 1e-4.</span>
            </a>
        </td>
                <td class="value">&#x27;cyclic&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>Gradient Boosting</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-119" type="checkbox" ><label for="sk-estimator-id-119" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Gradient Boosting__columntransformer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformers',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples">
                transformers
                <span class="param-doc-description">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>
            </a>
        </td>
                <td class="value">[(&#x27;simpleimputer&#x27;, ...), (&#x27;ordinalencoder&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('remainder',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27">
                remainder
                <span class="param-doc-description">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>
            </a>
        </td>
                <td class="value">&#x27;drop&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('sparse_threshold',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3">
                sparse_threshold
                <span class="param-doc-description">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>
            </a>
        </td>
                <td class="value">0.3</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_jobs',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone">
                n_jobs
                <span class="param-doc-description">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transformer_weights',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone">
                transformer_weights
                <span class="param-doc-description">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose_feature_names_out',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue">
                verbose_feature_names_out
                <span class="param-doc-description">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out="{transformer_name}__{feature_name}"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``"{feature_name}__{transformer_name}"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('force_int_remainder_cols',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse">
                force_int_remainder_cols
                <span class="param-doc-description">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>
            </a>
        </td>
                <td class="value">&#x27;deprecated&#x27;</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-120" type="checkbox" ><label for="sk-estimator-id-120" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>simpleimputer</div></div></label><div class="sk-toggleable__content " data-param-prefix="Gradient Boosting__columntransformer__simpleimputer__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46c710&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-121" type="checkbox" ><label for="sk-estimator-id-121" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Gradient Boosting__columntransformer__simpleimputer__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('missing_values',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan">
                missing_values
                <span class="param-doc-description">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>
            </a>
        </td>
                <td class="value">nan</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('strategy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27">
                strategy
                <span class="param-doc-description">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If "mean", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If "median", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If "most_frequent", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If "constant", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy="constant" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>
            </a>
        </td>
                <td class="value">&#x27;mean&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fill_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone">
                fill_value
                <span class="param-doc-description">fill_value: str or numerical value, default=None<br><br>When strategy == "constant", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and "missing_value" for strings or object data types.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue">
                copy
                <span class="param-doc-description">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('add_indicator',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse">
                add_indicator
                <span class="param-doc-description">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('keep_empty_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse">
                keep_empty_features
                <span class="param-doc-description">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy="constant"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-122" type="checkbox" ><label for="sk-estimator-id-122" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>ordinalencoder</div></div></label><div class="sk-toggleable__content " data-param-prefix="Gradient Boosting__columntransformer__ordinalencoder__"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7fe89c46de10&gt;</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-123" type="checkbox" ><label for="sk-estimator-id-123" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Gradient Boosting__columntransformer__ordinalencoder__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27">
                categories
                <span class="param-doc-description">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values, and should be sorted in case of numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('dtype',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64">
                dtype
                <span class="param-doc-description">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>
            </a>
        </td>
                <td class="value">&lt;class &#x27;numpy.float64&#x27;&gt;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('handle_unknown',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27use_encoded_value%27%7D%2C%20default%3D%27error%27">
                handle_unknown
                <span class="param-doc-description">handle_unknown: {'error', 'use_encoded_value'}, default='error'<br><br>When set to 'error' an error will be raised in case an unknown<br>categorical feature is present during transform. When set to<br>'use_encoded_value', the encoded value of unknown categories will be<br>set to the value given for the parameter `unknown_value`. In<br>:meth:`inverse_transform`, an unknown category will be denoted as None.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">&#x27;use_encoded_value&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('unknown_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=unknown_value,-int%20or%20np.nan%2C%20default%3DNone">
                unknown_value
                <span class="param-doc-description">unknown_value: int or np.nan, default=None<br><br>When the parameter handle_unknown is set to 'use_encoded_value', this<br>parameter is required and will set the encoded value of unknown<br>categories. It has to be distinct from the values used to encode any of<br>the categories in `fit`. If set to np.nan, the `dtype` parameter must<br>be a float dtype.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('encoded_missing_value',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=encoded_missing_value,-int%20or%20np.nan%2C%20default%3Dnp.nan">
                encoded_missing_value
                <span class="param-doc-description">encoded_missing_value: int or np.nan, default=np.nan<br><br>Encoded value of missing categories. If set to `np.nan`, then the `dtype`<br>parameter must be a float dtype.<br><br>.. versionadded:: 1.1</span>
            </a>
        </td>
                <td class="value">-2</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_frequency',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone">
                min_frequency
                <span class="param-doc-description">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_categories',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone">
                max_categories
                <span class="param-doc-description">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output categories for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>`max_categories` do **not** take into account missing or unknown<br>categories. Setting `unknown_value` or `encoded_missing_value` to an<br>integer will increase the number of unique integer codes by one each.<br>This can result in up to `max_categories + 2` integer codes.<br><br>.. versionadded:: 1.3<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-124" type="checkbox" ><label for="sk-estimator-id-124" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="Gradient Boosting__histgradientboostingregressor__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('loss',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=loss,-%7B%27squared_error%27%2C%20%27absolute_error%27%2C%20%27gamma%27%2C%20%27poisson%27%2C%20%27quantile%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27squared_error%27">
                loss
                <span class="param-doc-description">loss: {'squared_error', 'absolute_error', 'gamma', 'poisson', 'quantile'},             default='squared_error'<br><br>The loss function to use in the boosting process. Note that the<br>"squared error", "gamma" and "poisson" losses actually implement<br>"half least squares loss", "half gamma deviance" and "half poisson<br>deviance" to simplify the computation of the gradient. Furthermore,<br>"gamma" and "poisson" losses internally use a log-link, "gamma"<br>requires ``y > 0`` and "poisson" requires ``y >= 0``.<br>"quantile" uses the pinball loss.<br><br>.. versionchanged:: 0.23<br>   Added option 'poisson'.<br><br>.. versionchanged:: 1.1<br>   Added option 'quantile'.<br><br>.. versionchanged:: 1.3<br>   Added option 'gamma'.</span>
            </a>
        </td>
                <td class="value">&#x27;squared_error&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('quantile',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=quantile,-float%2C%20default%3DNone">
                quantile
                <span class="param-doc-description">quantile: float, default=None<br><br>If loss is "quantile", this parameter specifies which quantile to be estimated<br>and must be between 0 and 1.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('learning_rate',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=learning_rate,-float%2C%20default%3D0.1">
                learning_rate
                <span class="param-doc-description">learning_rate: float, default=0.1<br><br>The learning rate, also known as *shrinkage*. This is used as a<br>multiplicative factor for the leaves values. Use ``1`` for no<br>shrinkage.</span>
            </a>
        </td>
                <td class="value">0.1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_iter,-int%2C%20default%3D100">
                max_iter
                <span class="param-doc-description">max_iter: int, default=100<br><br>The maximum number of iterations of the boosting process, i.e. the<br>maximum number of trees.</span>
            </a>
        </td>
                <td class="value">100</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_leaf_nodes',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_leaf_nodes,-int%20or%20None%2C%20default%3D31">
                max_leaf_nodes
                <span class="param-doc-description">max_leaf_nodes: int or None, default=31<br><br>The maximum number of leaves for each tree. Must be strictly greater<br>than 1. If None, there is no maximum limit.</span>
            </a>
        </td>
                <td class="value">31</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_depth',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_depth,-int%20or%20None%2C%20default%3DNone">
                max_depth
                <span class="param-doc-description">max_depth: int or None, default=None<br><br>The maximum depth of each tree. The depth of a tree is the number of<br>edges to go from the root to the deepest leaf.<br>Depth isn't constrained by default.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('min_samples_leaf',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=min_samples_leaf,-int%2C%20default%3D20">
                min_samples_leaf
                <span class="param-doc-description">min_samples_leaf: int, default=20<br><br>The minimum number of samples per leaf. For small datasets with less<br>than a few hundred samples, it is recommended to lower this value<br>since only very shallow trees would be built.</span>
            </a>
        </td>
                <td class="value">20</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('l2_regularization',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=l2_regularization,-float%2C%20default%3D0">
                l2_regularization
                <span class="param-doc-description">l2_regularization: float, default=0<br><br>The L2 regularization parameter penalizing leaves with small hessians.<br>Use ``0`` for no regularization (default).</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_features,-float%2C%20default%3D1.0">
                max_features
                <span class="param-doc-description">max_features: float, default=1.0<br><br>Proportion of randomly chosen features in each and every node split.<br>This is a form of regularization, smaller values make the trees weaker<br>learners and might prevent overfitting.<br>If interaction constraints from `interaction_cst` are present, only allowed<br>features are taken into account for the subsampling.<br><br>.. versionadded:: 1.4</span>
            </a>
        </td>
                <td class="value">1.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_bins',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=max_bins,-int%2C%20default%3D255">
                max_bins
                <span class="param-doc-description">max_bins: int, default=255<br><br>The maximum number of bins to use for non-missing values. Before<br>training, each feature of the input array `X` is binned into<br>integer-valued bins, which allows for a much faster training stage.<br>Features with a small number of unique values may use less than<br>``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin<br>is always reserved for missing values. Must be no larger than 255.</span>
            </a>
        </td>
                <td class="value">255</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('categorical_features',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=categorical_features,-array-like%20of%20%7Bbool%2C%20int%2C%20str%7D%20of%20shape%20%28n_features%29%20%20%20%20%20%20%20%20%20%20%20%20%20or%20shape%20%28n_categorical_features%2C%29%2C%20default%3D%27from_dtype%27">
                categorical_features
                <span class="param-doc-description">categorical_features: array-like of {bool, int, str} of shape (n_features)             or shape (n_categorical_features,), default='from_dtype'<br><br>Indicates the categorical features.<br><br>- None : no feature will be considered categorical.<br>- boolean array-like : boolean mask indicating categorical features.<br>- integer array-like : integer indices indicating categorical<br>  features.<br>- str array-like: names of categorical features (assuming the training<br>  data has feature names).<br>- `"from_dtype"`: dataframe columns with dtype "category" are<br>  considered to be categorical features. The input must be an object<br>  exposing a ``__dataframe__`` method such as pandas or polars<br>  DataFrames to use this feature.<br><br>For each categorical feature, there must be at most `max_bins` unique<br>categories. Negative values for categorical features encoded as numeric<br>dtypes are treated as missing values. All categorical values are<br>converted to floating point numbers. This means that categorical values<br>of 1.0 and 1 are treated as the same category.<br><br>Read more in the :ref:`User Guide <categorical_support_gbdt>` and<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`.<br><br>.. versionadded:: 0.24<br><br>.. versionchanged:: 1.2<br>   Added support for feature names.<br><br>.. versionchanged:: 1.4<br>   Added `"from_dtype"` option.<br><br>.. versionchanged:: 1.6<br>   The default value changed from `None` to `"from_dtype"`.</span>
            </a>
        </td>
                <td class="value">&#x27;from_dtype&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('monotonic_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%20or%20dict%2C%20default%3DNone">
                monotonic_cst
                <span class="param-doc-description">monotonic_cst: array-like of int of shape (n_features) or dict, default=None<br><br>Monotonic constraint to enforce on each feature are specified using the<br>following integer values:<br><br>- 1: monotonic increase<br>- 0: no constraint<br>- -1: monotonic decrease<br><br>If a dict with str keys, map feature to monotonic constraints by name.<br>If an array, the features are mapped to constraints by position. See<br>:ref:`monotonic_cst_features_names` for a usage example.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 0.23<br><br>.. versionchanged:: 1.2<br>   Accept dict of constraints with feature names as keys.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('interaction_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=interaction_cst,-%7B%22pairwise%22%2C%20%22no_interactions%22%7D%20or%20sequence%20of%20lists/tuples/sets%20%20%20%20%20%20%20%20%20%20%20%20%20of%20int%2C%20default%3DNone">
                interaction_cst
                <span class="param-doc-description">interaction_cst: {"pairwise", "no_interactions"} or sequence of lists/tuples/sets             of int, default=None<br><br>Specify interaction constraints, the sets of features which can<br>interact with each other in child node splits.<br><br>Each item specifies the set of feature indices that are allowed<br>to interact with each other. If there are more features than<br>specified in these constraints, they are treated as if they were<br>specified as an additional set.<br><br>The strings "pairwise" and "no_interactions" are shorthands for<br>allowing only pairwise or no interactions, respectively.<br><br>For instance, with 5 features in total, `interaction_cst=[{0, 1}]`<br>is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`,<br>and specifies that each branch of a tree will either only split<br>on features 0 and 1 or only split on features 2, 3 and 4.<br><br>See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`.<br><br>.. versionadded:: 1.2</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('warm_start',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
                warm_start
                <span class="param-doc-description">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble. For results to be valid, the<br>estimator should be re-trained on the same data only.<br>See :term:`the Glossary <warm_start>`.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('early_stopping',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=early_stopping,-%27auto%27%20or%20bool%2C%20default%3D%27auto%27">
                early_stopping
                <span class="param-doc-description">early_stopping: 'auto' or bool, default='auto'<br><br>If 'auto', early stopping is enabled if the sample size is larger than<br>10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping<br>is enabled, otherwise early stopping is disabled.<br><br>.. versionadded:: 0.23</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('scoring',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=scoring,-str%20or%20callable%20or%20None%2C%20default%3D%27loss%27">
                scoring
                <span class="param-doc-description">scoring: str or callable or None, default='loss'<br><br>Scoring method to use for early stopping. Only used if `early_stopping`<br>is enabled. Options:<br><br>- str: see :ref:`scoring_string_names` for options.<br>- callable: a scorer callable object (e.g., function) with signature<br>  ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details.<br>- `None`: the :ref:`coefficient of determination <r2_score>`<br>  (:math:`R^2`) is used.<br>- 'loss': early stopping is checked w.r.t the loss value.</span>
            </a>
        </td>
                <td class="value">&#x27;loss&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('validation_fraction',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=validation_fraction,-int%20or%20float%20or%20None%2C%20default%3D0.1">
                validation_fraction
                <span class="param-doc-description">validation_fraction: int or float or None, default=0.1<br><br>Proportion (or absolute size) of training data to set aside as<br>validation data for early stopping. If None, early stopping is done on<br>the training data.<br>The value is ignored if either early stopping is not performed, e.g.<br>`early_stopping=False`, or if `X_val` and `y_val` are passed to fit.</span>
            </a>
        </td>
                <td class="value">0.1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('n_iter_no_change',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=n_iter_no_change,-int%2C%20default%3D10">
                n_iter_no_change
                <span class="param-doc-description">n_iter_no_change: int, default=10<br><br>Used to determine when to "early stop". The fitting process is<br>stopped when none of the last ``n_iter_no_change`` scores are better<br>than the ``n_iter_no_change - 1`` -th-to-last one, up to some<br>tolerance. Only used if early stopping is performed.</span>
            </a>
        </td>
                <td class="value">10</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=tol,-float%2C%20default%3D1e-7">
                tol
                <span class="param-doc-description">tol: float, default=1e-7<br><br>The absolute tolerance to use when comparing scores during early<br>stopping. The higher the tolerance, the more likely we are to early<br>stop: higher tolerance means that it will be harder for subsequent<br>iterations to be considered an improvement upon the reference score.</span>
            </a>
        </td>
                <td class="value">1e-07</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>The verbosity level. If not zero, print some information about the<br>fitting process. ``1`` prints only summary info, ``2`` prints info per<br>iteration.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone">
                random_state
                <span class="param-doc-description">random_state: int, RandomState instance or None, default=None<br><br>Pseudo-random number generator to control the subsampling in the<br>binning process, and the train/validation data split if early stopping<br>is enabled.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>
            </a>
        </td>
                <td class="value">0</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><label>final_estimator</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-125" type="checkbox" ><label for="sk-estimator-id-125" class="sk-toggleable__label  sk-toggleable__label-arrow"><div><div>RidgeCV</div></div><div><a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html">?<span>Documentation for RidgeCV</span></a></div></label><div class="sk-toggleable__content " data-param-prefix="final_estimator__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('alphas',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=alphas,-array-like%20of%20shape%20%28n_alphas%2C%29%2C%20default%3D%280.1%2C%201.0%2C%2010.0%29">
                alphas
                <span class="param-doc-description">alphas: array-like of shape (n_alphas,), default=(0.1, 1.0, 10.0)<br><br>Array of alpha values to try.<br>Regularization strength; must be a positive float. Regularization<br>improves the conditioning of the problem and reduces the variance of<br>the estimates. Larger values specify stronger regularization.<br>Alpha corresponds to ``1 / (2C)`` in other linear models such as<br>:class:`~sklearn.linear_model.LogisticRegression` or<br>:class:`~sklearn.svm.LinearSVC`.<br>If using Leave-One-Out cross-validation, alphas must be strictly positive.</span>
            </a>
        </td>
                <td class="value">(0.1, ...)</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('fit_intercept',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
                fit_intercept
                <span class="param-doc-description">fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to false, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('scoring',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=scoring,-str%2C%20callable%2C%20default%3DNone">
                scoring
                <span class="param-doc-description">scoring: str, callable, default=None<br><br>The scoring method to use for cross-validation. Options:<br><br>- str: see :ref:`scoring_string_names` for options.<br>- callable: a scorer callable object (e.g., function) with signature<br>  ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details.<br>- `None`: negative :ref:`mean squared error <mean_squared_error>` if cv is<br>  None (i.e. when using leave-one-out cross-validation), or<br>  :ref:`coefficient of determination <r2_score>` (:math:`R^2`) otherwise.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('cv',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=cv,-int%2C%20cross-validation%20generator%20or%20an%20iterable%2C%20default%3DNone">
                cv
                <span class="param-doc-description">cv: int, cross-validation generator or an iterable, default=None<br><br>Determines the cross-validation splitting strategy.<br>Possible inputs for cv are:<br><br>- None, to use the efficient Leave-One-Out cross-validation<br>- integer, to specify the number of folds.<br>- :term:`CV splitter`,<br>- An iterable yielding (train, test) splits as arrays of indices.<br><br>For integer/None inputs, if ``y`` is binary or multiclass,<br>:class:`~sklearn.model_selection.StratifiedKFold` is used, else,<br>:class:`~sklearn.model_selection.KFold` is used.<br><br>Refer :ref:`User Guide <cross_validation>` for the various<br>cross-validation strategies that can be used here.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('gcv_mode',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=gcv_mode,-%7B%27auto%27%2C%20%27svd%27%2C%20%27eigen%27%7D%2C%20default%3D%27auto%27">
                gcv_mode
                <span class="param-doc-description">gcv_mode: {'auto', 'svd', 'eigen'}, default='auto'<br><br>Flag indicating which strategy to use when performing<br>Leave-One-Out Cross-Validation. Options are::<br><br>    'auto' : use 'svd' if n_samples > n_features, otherwise use 'eigen'<br>    'svd' : force use of singular value decomposition of X when X is<br>        dense, eigenvalue decomposition of X^T.X when X is sparse.<br>    'eigen' : force computation via eigendecomposition of X.X^T<br><br>The 'auto' mode is the default and is intended to pick the cheaper<br>option of the two depending on the shape of the training data.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('store_cv_results',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=store_cv_results,-bool%2C%20default%3DFalse">
                store_cv_results
                <span class="param-doc-description">store_cv_results: bool, default=False<br><br>Flag indicating if the cross-validation values corresponding to<br>each alpha should be stored in the ``cv_results_`` attribute (see<br>below). This flag is only compatible with ``cv=None`` (i.e. using<br>Leave-One-Out Cross-Validation).<br><br>.. versionchanged:: 1.5<br>    Parameter name changed from `store_cv_values` to `store_cv_results`.</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('alpha_per_target',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.RidgeCV.html#:~:text=alpha_per_target,-bool%2C%20default%3DFalse">
                alpha_per_target
                <span class="param-doc-description">alpha_per_target: bool, default=False<br><br>Flag indicating whether to optimize the alpha value (picked from the<br>`alphas` parameter list) for each target separately (for multi-output<br>settings: multiple prediction targets). When set to `True`, after<br>fitting, the `alpha_` attribute will contain a value for each target.<br>When set to `False`, a single alpha is used for all targets.<br><br>.. versionadded:: 0.24</span>
            </a>
        </td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
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.. GENERATED FROM PYTHON SOURCE LINES 198-204

Measure and plot the results
############################

Now we can use Ames Housing dataset to make the predictions. We check the
performance of each individual predictor as well as of the stack of the
regressors.

.. GENERATED FROM PYTHON SOURCE LINES 204-255

.. code-block:: Python



    import time

    import matplotlib.pyplot as plt

    from sklearn.metrics import PredictionErrorDisplay
    from sklearn.model_selection import cross_val_predict, cross_validate

    fig, axs = plt.subplots(2, 2, figsize=(9, 7))
    axs = np.ravel(axs)

    for ax, (name, est) in zip(
        axs, estimators + [("Stacking Regressor", stacking_regressor)]
    ):
        scorers = {"R2": "r2", "MAE": "neg_mean_absolute_error"}

        start_time = time.time()
        scores = cross_validate(
            est, X, y, scoring=list(scorers.values()), n_jobs=-1, verbose=0
        )
        elapsed_time = time.time() - start_time

        y_pred = cross_val_predict(est, X, y, n_jobs=-1, verbose=0)
        scores = {
            key: (
                f"{np.abs(np.mean(scores[f'test_{value}'])):.2f} +- "
                f"{np.std(scores[f'test_{value}']):.2f}"
            )
            for key, value in scorers.items()
        }

        display = PredictionErrorDisplay.from_predictions(
            y_true=y,
            y_pred=y_pred,
            kind="actual_vs_predicted",
            ax=ax,
            scatter_kwargs={"alpha": 0.2, "color": "tab:blue"},
            line_kwargs={"color": "tab:red"},
        )
        ax.set_title(f"{name}\nEvaluation in {elapsed_time:.2f} seconds")

        for name, score in scores.items():
            ax.plot([], [], " ", label=f"{name}: {score}")
        ax.legend(loc="upper left")

    plt.suptitle("Single predictors versus stacked predictors")
    plt.tight_layout()
    plt.subplots_adjust(top=0.9)
    plt.show()




.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png
   :alt: Single predictors versus stacked predictors, Random Forest Evaluation in 1.15 seconds, Lasso Evaluation in 0.25 seconds, Gradient Boosting Evaluation in 0.48 seconds, Stacking Regressor Evaluation in 9.45 seconds
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_stack_predictors_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 256-259

The stacked regressor will combine the strengths of the different regressors.
However, we also see that training the stacked regressor is much more
computationally expensive.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 22.386 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_stack_predictors.py:

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  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
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        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/ensemble/plot_stack_predictors.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_stack_predictors.ipynb <plot_stack_predictors.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_stack_predictors.py <plot_stack_predictors.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_stack_predictors.zip <plot_stack_predictors.zip>`


.. include:: plot_stack_predictors.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
