
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_voting_regressor.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_voting_regressor.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_voting_regressor.py:


=================================================
Plot individual and voting regression predictions
=================================================

.. currentmodule:: sklearn

A voting regressor is an ensemble meta-estimator that fits several base
regressors, each on the whole dataset. Then it averages the individual
predictions to form a final prediction.
We will use three different regressors to predict the data:
:class:`~ensemble.GradientBoostingRegressor`,
:class:`~ensemble.RandomForestRegressor`, and
:class:`~linear_model.LinearRegression`).
Then the above 3 regressors will be used for the
:class:`~ensemble.VotingRegressor`.

Finally, we will plot the predictions made by all models for comparison.

We will work with the diabetes dataset which consists of 10 features
collected from a cohort of diabetes patients. The target is a quantitative
measure of disease progression one year after baseline.

.. GENERATED FROM PYTHON SOURCE LINES 25-39

.. code-block:: Python


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

    import matplotlib.pyplot as plt

    from sklearn.datasets import load_diabetes
    from sklearn.ensemble import (
        GradientBoostingRegressor,
        RandomForestRegressor,
        VotingRegressor,
    )
    from sklearn.linear_model import LinearRegression








.. GENERATED FROM PYTHON SOURCE LINES 40-46

Training classifiers
--------------------------------

First, we will load the diabetes dataset and initiate a gradient boosting
regressor, a random forest regressor and a linear regression. Next, we will
use the 3 regressors to build the voting regressor:

.. GENERATED FROM PYTHON SOURCE LINES 46-61

.. code-block:: Python


    X, y = load_diabetes(return_X_y=True)

    # Train classifiers
    reg1 = GradientBoostingRegressor(random_state=1)
    reg2 = RandomForestRegressor(random_state=1)
    reg3 = LinearRegression()

    reg1.fit(X, y)
    reg2.fit(X, y)
    reg3.fit(X, y)

    ereg = VotingRegressor([("gb", reg1), ("rf", reg2), ("lr", reg3)])
    ereg.fit(X, y)






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    </style><body><div id="sk-container-id-34" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>VotingRegressor(estimators=[(&#x27;gb&#x27;, GradientBoostingRegressor(random_state=1)),
                                (&#x27;rf&#x27;, RandomForestRegressor(random_state=1)),
                                (&#x27;lr&#x27;, LinearRegression())])</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-141" type="checkbox" ><label for="sk-estimator-id-141" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>VotingRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingRegressor.html">?<span>Documentation for VotingRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
            <div class="estimator-table">
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                     onclick="copyToClipboard('estimators',
                              this.parentElement.nextElementSibling)"
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                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingRegressor.html#:~:text=estimators,-list%20of%20%28str%2C%20estimator%29%20tuples">
                estimators
                <span class="param-doc-description">estimators: list of (str, estimator) tuples<br><br>Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones<br>of those original estimators that will be stored in the class attribute<br>``self.estimators_``. An estimator can be set to ``'drop'`` using<br>:meth:`set_params`.<br><br>.. versionchanged:: 0.21<br>    ``'drop'`` is accepted. Using None was deprecated in 0.22 and<br>    support was removed in 0.24.</span>
            </a>
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                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.VotingRegressor.html#:~:text=weights,-array-like%20of%20shape%20%28n_regressors%2C%29%2C%20default%3DNone">
                weights
                <span class="param-doc-description">weights: array-like of shape (n_regressors,), default=None<br><br>Sequence of weights (`float` or `int`) to weight the occurrences of<br>predicted values before averaging. Uses uniform weights if `None`.</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.VotingRegressor.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``.<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('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.VotingRegressor.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 will be printed as it<br>is completed.<br><br>.. versionadded:: 0.23</span>
            </a>
        </td>
                <td class="value">False</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 fitted sk-toggleable"><label>gb</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-142" type="checkbox" ><label for="sk-estimator-id-142" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>GradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html">?<span>Documentation for GradientBoostingRegressor</span></a></div></label><div class="sk-toggleable__content fitted" data-param-prefix="gb__">
            <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.GradientBoostingRegressor.html#:~:text=loss,-%7B%27squared_error%27%2C%20%27absolute_error%27%2C%20%27huber%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', 'huber', 'quantile'},             default='squared_error'<br><br>Loss function to be optimized. 'squared_error' refers to the squared<br>error for regression. 'absolute_error' refers to the absolute error of<br>regression and is a robust loss function. 'huber' is a<br>combination of the two. 'quantile' allows quantile regression (use<br>`alpha` to specify the quantile).<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`<br>for an example that demonstrates quantile regression for creating<br>prediction intervals with `loss='quantile'`.</span>
            </a>
        </td>
                <td class="value">&#x27;squared_error&#x27;</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.GradientBoostingRegressor.html#:~:text=learning_rate,-float%2C%20default%3D0.1">
                learning_rate
                <span class="param-doc-description">learning_rate: float, default=0.1<br><br>Learning rate shrinks the contribution of each tree by `learning_rate`.<br>There is a trade-off between learning_rate and n_estimators.<br>Values must be in the range `[0.0, inf)`.</span>
            </a>
        </td>
                <td class="value">0.1</td>
            </tr>
    

            <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.GradientBoostingRegressor.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 boosting stages to perform. Gradient boosting<br>is fairly robust to over-fitting so a large number usually<br>results in better performance.<br>Values must be in the range `[1, inf)`.</span>
            </a>
        </td>
                <td class="value">100</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('subsample',
                              this.parentElement.nextElementSibling)"
                ></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.GradientBoostingRegressor.html#:~:text=subsample,-float%2C%20default%3D1.0">
                subsample
                <span class="param-doc-description">subsample: float, default=1.0<br><br>The fraction of samples to be used for fitting the individual base<br>learners. If smaller than 1.0 this results in Stochastic Gradient<br>Boosting. `subsample` interacts with the parameter `n_estimators`.<br>Choosing `subsample < 1.0` leads to a reduction of variance<br>and an increase in bias.<br>Values must be in the range `(0.0, 1.0]`.</span>
            </a>
        </td>
                <td class="value">1.0</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.GradientBoostingRegressor.html#:~:text=criterion,-%7B%27friedman_mse%27%2C%20%27squared_error%27%7D%2C%20default%3D%27friedman_mse%27">
                criterion
                <span class="param-doc-description">criterion: {'friedman_mse', 'squared_error'}, default='friedman_mse'<br><br>The function to measure the quality of a split. Supported criteria are<br>"friedman_mse" for the mean squared error with improvement score by<br>Friedman, "squared_error" for mean squared error. The default value of<br>"friedman_mse" is generally the best as it can provide a better<br>approximation in some cases.<br><br>.. versionadded:: 0.18</span>
            </a>
        </td>
                <td class="value">&#x27;friedman_mse&#x27;</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.GradientBoostingRegressor.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, values must be in the range `[2, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and `min_samples_split`<br>  will be `ceil(min_samples_split * n_samples)`.<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.GradientBoostingRegressor.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, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf`<br>  will be `ceil(min_samples_leaf * n_samples)`.<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.GradientBoostingRegressor.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.<br>Values must be in the range `[0.0, 0.5]`.</span>
            </a>
        </td>
                <td class="value">0.0</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.GradientBoostingRegressor.html#:~:text=max_depth,-int%20or%20None%2C%20default%3D3">
                max_depth
                <span class="param-doc-description">max_depth: int or None, default=3<br><br>Maximum depth of the individual regression estimators. The maximum<br>depth limits the number of nodes in the tree. Tune this parameter<br>for best performance; the best value depends on the interaction<br>of the input variables. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br>If int, values must be in the range `[1, inf)`.</span>
            </a>
        </td>
                <td class="value">3</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.GradientBoostingRegressor.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>Values must be in the range `[0.0, inf)`.<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('init',
                              this.parentElement.nextElementSibling)"
                ></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.GradientBoostingRegressor.html#:~:text=init,-estimator%20or%20%27zero%27%2C%20default%3DNone">
                init
                <span class="param-doc-description">init: estimator or 'zero', default=None<br><br>An estimator object that is used to compute the initial predictions.<br>``init`` has to provide :term:`fit` and :term:`predict`. If 'zero', the<br>initial raw predictions are set to zero. By default a<br>``DummyEstimator`` is used, predicting either the average target value<br>(for loss='squared_error'), or a quantile for the other losses.</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.GradientBoostingRegressor.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 the random seed given to each Tree estimator at each<br>boosting iteration.<br>In addition, it controls the random permutation of the features at<br>each split (see Notes for more details).<br>It also controls the random splitting of the training data to obtain a<br>validation set if `n_iter_no_change` is not None.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary <random_state>`.</span>
            </a>
        </td>
                <td class="value">1</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.GradientBoostingRegressor.html#:~:text=max_features,-%7B%27sqrt%27%2C%20%27log2%27%7D%2C%20int%20or%20float%2C%20default%3DNone">
                max_features
                <span class="param-doc-description">max_features: {'sqrt', 'log2'}, int or float, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, values must be in the range `[1, inf)`.<br>- If float, values must be in the range `(0.0, 1.0]` and the features<br>  considered at each split will be `max(1, int(max_features * n_features_in_))`.<br>- If "sqrt", then `max_features=sqrt(n_features)`.<br>- If "log2", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Choosing `max_features < n_features` leads to a reduction of variance<br>and an increase in bias.<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">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('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.GradientBoostingRegressor.html#:~:text=alpha,-float%2C%20default%3D0.9">
                alpha
                <span class="param-doc-description">alpha: float, default=0.9<br><br>The alpha-quantile of the huber loss function and the quantile<br>loss function. Only if ``loss='huber'`` or ``loss='quantile'``.<br>Values must be in the range `(0.0, 1.0)`.</span>
            </a>
        </td>
                <td class="value">0.9</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.GradientBoostingRegressor.html#:~:text=verbose,-int%2C%20default%3D0">
                verbose
                <span class="param-doc-description">verbose: int, default=0<br><br>Enable verbose output. If 1 then it prints progress and performance<br>once in a while (the more trees the lower the frequency). If greater<br>than 1 then it prints progress and performance for every tree.<br>Values must be in the range `[0, inf)`.</span>
            </a>
        </td>
                <td class="value">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.GradientBoostingRegressor.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>Values must be in the range `[2, inf)`.<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('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.GradientBoostingRegressor.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 erase the<br>previous solution. 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('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.GradientBoostingRegressor.html#:~:text=validation_fraction,-float%2C%20default%3D0.1">
                validation_fraction
                <span class="param-doc-description">validation_fraction: float, default=0.1<br><br>The proportion of training data to set aside as validation set for<br>early stopping. Values must be in the range `(0.0, 1.0)`.<br>Only used if ``n_iter_no_change`` is set to an integer.<br><br>.. versionadded:: 0.20</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.GradientBoostingRegressor.html#:~:text=n_iter_no_change,-int%2C%20default%3DNone">
                n_iter_no_change
                <span class="param-doc-description">n_iter_no_change: int, default=None<br><br>``n_iter_no_change`` is used to decide if early stopping will be used<br>to terminate training when validation score is not improving. By<br>default it is set to None to disable early stopping. If set to a<br>number, it will set aside ``validation_fraction`` size of the training<br>data as validation and terminate training when validation score is not<br>improving in all of the previous ``n_iter_no_change`` numbers of<br>iterations.<br>Values must be in the range `[1, inf)`.<br>See<br>:ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`.<br><br>.. versionadded:: 0.20</span>
            </a>
        </td>
                <td class="value">None</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.GradientBoostingRegressor.html#:~:text=tol,-float%2C%20default%3D1e-4">
                tol
                <span class="param-doc-description">tol: float, default=1e-4<br><br>Tolerance for the early stopping. When the loss is not improving<br>by at least tol for ``n_iter_no_change`` iterations (if set to a<br>number), the training stops.<br>Values must be in the range `[0.0, inf)`.<br><br>.. versionadded:: 0.20</span>
            </a>
        </td>
                <td class="value">0.0001</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.GradientBoostingRegressor.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.<br>Values must be in the range `[0.0, inf)`.<br>See :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>
    
                      </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 fitted sk-toggleable"><label>rf</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-143" type="checkbox" ><label for="sk-estimator-id-143" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestRegressor</div></div><div><a class="sk-estimator-doc-link fitted" 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 fitted" data-param-prefix="rf__">
            <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">1</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 class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>lr</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-144" type="checkbox" ><label for="sk-estimator-id-144" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a></div></label><div class="sk-toggleable__content fitted" data-param-prefix="lr__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <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.LinearRegression.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('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.LinearRegression.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('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.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6">
                tol
                <span class="param-doc-description">tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies a different convergence criterion for the `lsqr` solver.<br>`tol` is set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. This parameter has no effect when fitting<br>on dense data.<br><br>.. versionadded:: 1.7</span>
            </a>
        </td>
                <td class="value">1e-06</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.LinearRegression.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 use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets > 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>
            </a>
        </td>
                <td class="value">None</td>
            </tr>
    

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                <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.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse">
                positive
                <span class="param-doc-description">positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>
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                <td class="value">False</td>
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.. GENERATED FROM PYTHON SOURCE LINES 62-66

Making predictions
--------------------------------

Now we will use each of the regressors to make the 20 first predictions.

.. GENERATED FROM PYTHON SOURCE LINES 66-74

.. code-block:: Python


    xt = X[:20]

    pred1 = reg1.predict(xt)
    pred2 = reg2.predict(xt)
    pred3 = reg3.predict(xt)
    pred4 = ereg.predict(xt)








.. GENERATED FROM PYTHON SOURCE LINES 75-80

Plot the results
--------------------------------

Finally, we will visualize the 20 predictions. The red stars show the average
prediction made by :class:`~ensemble.VotingRegressor`.

.. GENERATED FROM PYTHON SOURCE LINES 80-94

.. code-block:: Python


    plt.figure()
    plt.plot(pred1, "gd", label="GradientBoostingRegressor")
    plt.plot(pred2, "b^", label="RandomForestRegressor")
    plt.plot(pred3, "ys", label="LinearRegression")
    plt.plot(pred4, "r*", ms=10, label="VotingRegressor")

    plt.tick_params(axis="x", which="both", bottom=False, top=False, labelbottom=False)
    plt.ylabel("predicted")
    plt.xlabel("training samples")
    plt.legend(loc="best")
    plt.title("Regressor predictions and their average")

    plt.show()



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_regressor_001.png
   :alt: Regressor predictions and their average
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_regressor_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.828 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.8.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb
        :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_voting_regressor.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_voting_regressor.recommendations


.. only:: html

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

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