
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/release_highlights/plot_release_highlights_0_24_0.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_release_highlights_plot_release_highlights_0_24_0.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_release_highlights_plot_release_highlights_0_24_0.py:


========================================
Release Highlights for scikit-learn 0.24
========================================

.. currentmodule:: sklearn

We are pleased to announce the release of scikit-learn 0.24! Many bug fixes
and improvements were added, as well as some new key features. We detail
below a few of the major features of this release. **For an exhaustive list of
all the changes**, please refer to the :ref:`release notes <release_notes_0_24>`.

To install the latest version (with pip)::

    pip install --upgrade scikit-learn

or with conda::

    conda install -c conda-forge scikit-learn

.. GENERATED FROM PYTHON SOURCE LINES 25-51

Successive Halving estimators for tuning hyper-parameters
---------------------------------------------------------
Successive Halving, a state of the art method, is now available to
explore the space of the parameters and identify their best combination.
:class:`~sklearn.model_selection.HalvingGridSearchCV` and
:class:`~sklearn.model_selection.HalvingRandomSearchCV` can be
used as drop-in replacement for
:class:`~sklearn.model_selection.GridSearchCV` and
:class:`~sklearn.model_selection.RandomizedSearchCV`.
Successive Halving is an iterative selection process illustrated in the
figure below. The first iteration is run with a small amount of resources,
where the resource typically corresponds to the number of training samples,
but can also be an arbitrary integer parameter such as `n_estimators` in a
random forest. Only a subset of the parameter candidates are selected for the
next iteration, which will be run with an increasing amount of allocated
resources. Only a subset of candidates will last until the end of the
iteration process, and the best parameter candidate is the one that has the
highest score on the last iteration.

Read more in the :ref:`User Guide <successive_halving_user_guide>` (note:
the Successive Halving estimators are still :term:`experimental
<experimental>`).

.. figure:: ../model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png
  :target: ../model_selection/plot_successive_halving_iterations.html
  :align: center

.. GENERATED FROM PYTHON SOURCE LINES 51-80

.. code-block:: Python


    import numpy as np
    from scipy.stats import randint

    from sklearn.datasets import make_classification
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.experimental import enable_halving_search_cv  # noqa: F401
    from sklearn.model_selection import HalvingRandomSearchCV

    rng = np.random.RandomState(0)

    X, y = make_classification(n_samples=700, random_state=rng)

    clf = RandomForestClassifier(n_estimators=10, random_state=rng)

    param_dist = {
        "max_depth": [3, None],
        "max_features": randint(1, 11),
        "min_samples_split": randint(2, 11),
        "bootstrap": [True, False],
        "criterion": ["gini", "entropy"],
    }

    rsh = HalvingRandomSearchCV(
        estimator=clf, param_distributions=param_dist, factor=2, random_state=rng
    )
    rsh.fit(X, y)
    rsh.best_params_





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

 .. code-block:: none


    {'bootstrap': True, 'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10}



.. GENERATED FROM PYTHON SOURCE LINES 81-100

Native support for categorical features in HistGradientBoosting estimators
--------------------------------------------------------------------------
:class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
:class:`~sklearn.ensemble.HistGradientBoostingRegressor` now have native
support for categorical features: they can consider splits on non-ordered,
categorical data. Read more in the :ref:`User Guide
<categorical_support_gbdt>`.

.. figure:: ../ensemble/images/sphx_glr_plot_gradient_boosting_categorical_001.png
  :target: ../ensemble/plot_gradient_boosting_categorical.html
  :align: center

The plot shows that the new native support for categorical features leads to
fitting times that are comparable to models where the categories are treated
as ordered quantities, i.e. simply ordinal-encoded. Native support is also
more expressive than both one-hot encoding and ordinal encoding. However, to
use the new `categorical_features` parameter, it is still required to
preprocess the data within a pipeline as demonstrated in this :ref:`example
<sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py>`.

.. GENERATED FROM PYTHON SOURCE LINES 102-110

Improved performances of HistGradientBoosting estimators
--------------------------------------------------------
The memory footprint of :class:`ensemble.HistGradientBoostingRegressor` and
:class:`ensemble.HistGradientBoostingClassifier` has been significantly
improved during calls to `fit`. In addition, histogram initialization is now
done in parallel which results in slight speed improvements.
See more in the `Benchmark page
<https://scikit-learn.org/scikit-learn-benchmarks/>`_.

.. GENERATED FROM PYTHON SOURCE LINES 112-120

New self-training meta-estimator
--------------------------------
A new self-training implementation, based on `Yarowski's algorithm
<https://doi.org/10.3115/981658.981684>`_ can now be used with any
classifier that implements :term:`predict_proba`. The sub-classifier
will behave as a
semi-supervised classifier, allowing it to learn from unlabeled data.
Read more in the :ref:`User guide <self_training>`.

.. GENERATED FROM PYTHON SOURCE LINES 120-135

.. code-block:: Python


    import numpy as np

    from sklearn import datasets
    from sklearn.semi_supervised import SelfTrainingClassifier
    from sklearn.svm import SVC

    rng = np.random.RandomState(42)
    iris = datasets.load_iris()
    random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
    iris.target[random_unlabeled_points] = -1
    svc = SVC(probability=True, gamma="auto")
    self_training_model = SelfTrainingClassifier(svc)
    self_training_model.fit(iris.data, iris.target)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-8 {
      /* 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-8.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-8.dark {
      --sklearn-color-text-on-default-background: white;
      --sklearn-color-background: #111;
      --sklearn-color-border-box: white;
      --sklearn-color-icon: #878787;
    }

    #sk-container-id-8 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-8 pre {
      padding: 0;
    }

    #sk-container-id-8 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-8 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-8 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-8 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-8 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-8 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-8 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-8 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-8 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-8 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-8 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-8 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-8 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-8 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-8 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-8 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-8 div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-8 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-8 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-8 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-8 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-8 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-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-8 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-8 div.sk-label label.sk-toggleable__label,
    #sk-container-id-8 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-8 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-8 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-8 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-8 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-8 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-8 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-8 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-8 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-8 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-8 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-8 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-8 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 {
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    </style><body><div id="sk-container-id-8" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SelfTrainingClassifier(estimator=SVC(gamma=&#x27;auto&#x27;, probability=True))</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-22" type="checkbox" ><label for="sk-estimator-id-22" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SelfTrainingClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html">?<span>Documentation for SelfTrainingClassifier</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">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('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.semi_supervised.SelfTrainingClassifier.html#:~:text=estimator,-estimator%20object">
                estimator
                <span class="param-doc-description">estimator: estimator object<br><br>An estimator object implementing `fit` and `predict_proba`.<br>Invoking the `fit` method will fit a clone of the passed estimator,<br>which will be stored in the `estimator_` attribute.<br><br>.. versionadded:: 1.6<br>    `estimator` was added to replace `base_estimator`.</span>
            </a>
        </td>
                <td class="value">SVC(gamma=&#x27;au...bability=True)</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('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.semi_supervised.SelfTrainingClassifier.html#:~:text=threshold,-float%2C%20default%3D0.75">
                threshold
                <span class="param-doc-description">threshold: float, default=0.75<br><br>The decision threshold for use with `criterion='threshold'`.<br>Should be in [0, 1). When using the `'threshold'` criterion, a<br>:ref:`well calibrated classifier <calibration>` should be used.</span>
            </a>
        </td>
                <td class="value">0.75</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.semi_supervised.SelfTrainingClassifier.html#:~:text=criterion,-%7B%27threshold%27%2C%20%27k_best%27%7D%2C%20default%3D%27threshold%27">
                criterion
                <span class="param-doc-description">criterion: {'threshold', 'k_best'}, default='threshold'<br><br>The selection criterion used to select which labels to add to the<br>training set. If `'threshold'`, pseudo-labels with prediction<br>probabilities above `threshold` are added to the dataset. If `'k_best'`,<br>the `k_best` pseudo-labels with highest prediction probabilities are<br>added to the dataset. When using the 'threshold' criterion, a<br>:ref:`well calibrated classifier <calibration>` should be used.</span>
            </a>
        </td>
                <td class="value">&#x27;threshold&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('k_best',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html#:~:text=k_best,-int%2C%20default%3D10">
                k_best
                <span class="param-doc-description">k_best: int, default=10<br><br>The amount of samples to add in each iteration. Only used when<br>`criterion='k_best'`.</span>
            </a>
        </td>
                <td class="value">10</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.semi_supervised.SelfTrainingClassifier.html#:~:text=max_iter,-int%20or%20None%2C%20default%3D10">
                max_iter
                <span class="param-doc-description">max_iter: int or None, default=10<br><br>Maximum number of iterations allowed. Should be greater than or equal<br>to 0. If it is `None`, the classifier will continue to predict labels<br>until no new pseudo-labels are added, or all unlabeled samples have<br>been labeled.</span>
            </a>
        </td>
                <td class="value">10</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.semi_supervised.SelfTrainingClassifier.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>Enable verbose output.</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"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>estimator: SVC</div></div></label><div class="sk-toggleable__content fitted" data-param-prefix="estimator__"><pre>SVC(gamma=&#x27;auto&#x27;, probability=True)</pre></div></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-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SVC</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a></div></label><div class="sk-toggleable__content fitted" data-param-prefix="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('C',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=C,-float%2C%20default%3D1.0">
                C
                <span class="param-doc-description">C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive. The penalty<br>is a squared l2 penalty. For an intuitive visualization of the effects<br>of scaling the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>
            </a>
        </td>
                <td class="value">1.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('kernel',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=kernel,-%7B%27linear%27%2C%20%27poly%27%2C%20%27rbf%27%2C%20%27sigmoid%27%2C%20%27precomputed%27%7D%20or%20callable%2C%20%20%20%20%20%20%20%20%20%20default%3D%27rbf%27">
                kernel
                <span class="param-doc-description">kernel: {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable,          default='rbf'<br><br>Specifies the kernel type to be used in the algorithm. If<br>none is given, 'rbf' will be used. If a callable is given it is used to<br>pre-compute the kernel matrix from data matrices; that matrix should be<br>an array of shape ``(n_samples, n_samples)``. For an intuitive<br>visualization of different kernel types see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_kernels.py`.</span>
            </a>
        </td>
                <td class="value">&#x27;rbf&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('degree',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=degree,-int%2C%20default%3D3">
                degree
                <span class="param-doc-description">degree: int, default=3<br><br>Degree of the polynomial kernel function ('poly').<br>Must be non-negative. Ignored by all other kernels.</span>
            </a>
        </td>
                <td class="value">3</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('gamma',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=gamma,-%7B%27scale%27%2C%20%27auto%27%7D%20or%20float%2C%20default%3D%27scale%27">
                gamma
                <span class="param-doc-description">gamma: {'scale', 'auto'} or float, default='scale'<br><br>Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.<br><br>- if ``gamma='scale'`` (default) is passed then it uses<br>  1 / (n_features * X.var()) as value of gamma,<br>- if 'auto', uses 1 / n_features<br>- if float, must be non-negative.<br><br>.. versionchanged:: 0.22<br>   The default value of ``gamma`` changed from 'auto' to 'scale'.</span>
            </a>
        </td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('coef0',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=coef0,-float%2C%20default%3D0.0">
                coef0
                <span class="param-doc-description">coef0: float, default=0.0<br><br>Independent term in kernel function.<br>It is only significant in 'poly' and 'sigmoid'.</span>
            </a>
        </td>
                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('shrinking',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=shrinking,-bool%2C%20default%3DTrue">
                shrinking
                <span class="param-doc-description">shrinking: bool, default=True<br><br>Whether to use the shrinking heuristic.<br>See the :ref:`User Guide <shrinking_svm>`.</span>
            </a>
        </td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('probability',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=probability,-bool%2C%20default%3DFalse">
                probability
                <span class="param-doc-description">probability: bool, default=False<br><br>Whether to enable probability estimates. This must be enabled prior<br>to calling `fit`, will slow down that method as it internally uses<br>5-fold cross-validation, and `predict_proba` may be inconsistent with<br>`predict`. Read more in the :ref:`User Guide <scores_probabilities>`.</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.svm.SVC.html#:~:text=tol,-float%2C%20default%3D1e-3">
                tol
                <span class="param-doc-description">tol: float, default=1e-3<br><br>Tolerance for stopping criterion.</span>
            </a>
        </td>
                <td class="value">0.001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('cache_size',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=cache_size,-float%2C%20default%3D200">
                cache_size
                <span class="param-doc-description">cache_size: float, default=200<br><br>Specify the size of the kernel cache (in MB).</span>
            </a>
        </td>
                <td class="value">200</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('class_weight',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
                class_weight
                <span class="param-doc-description">class_weight: dict or 'balanced', default=None<br><br>Set the parameter C of class i to class_weight[i]*C for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</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.svm.SVC.html#:~:text=verbose,-bool%2C%20default%3DFalse">
                verbose
                <span class="param-doc-description">verbose: bool, default=False<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in libsvm that, if enabled, may not work<br>properly in a multithreaded context.</span>
            </a>
        </td>
                <td class="value">False</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.svm.SVC.html#:~:text=max_iter,-int%2C%20default%3D-1">
                max_iter
                <span class="param-doc-description">max_iter: int, default=-1<br><br>Hard limit on iterations within solver, or -1 for no limit.</span>
            </a>
        </td>
                <td class="value">-1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('decision_function_shape',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=decision_function_shape,-%7B%27ovo%27%2C%20%27ovr%27%7D%2C%20default%3D%27ovr%27">
                decision_function_shape
                <span class="param-doc-description">decision_function_shape: {'ovo', 'ovr'}, default='ovr'<br><br>Whether to return a one-vs-rest ('ovr') decision function of shape<br>(n_samples, n_classes) as all other classifiers, or the original<br>one-vs-one ('ovo') decision function of libsvm which has shape<br>(n_samples, n_classes * (n_classes - 1) / 2). However, note that<br>internally, one-vs-one ('ovo') is always used as a multi-class strategy<br>to train models; an ovr matrix is only constructed from the ovo matrix.<br>The parameter is ignored for binary classification.<br><br>.. versionchanged:: 0.19<br>    decision_function_shape is 'ovr' by default.<br><br>.. versionadded:: 0.17<br>   *decision_function_shape='ovr'* is recommended.<br><br>.. versionchanged:: 0.17<br>   Deprecated *decision_function_shape='ovo' and None*.</span>
            </a>
        </td>
                <td class="value">&#x27;ovr&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('break_ties',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.svm.SVC.html#:~:text=break_ties,-bool%2C%20default%3DFalse">
                break_ties
                <span class="param-doc-description">break_ties: bool, default=False<br><br>If true, ``decision_function_shape='ovr'``, and number of classes > 2,<br>:term:`predict` will break ties according to the confidence values of<br>:term:`decision_function`; otherwise the first class among the tied<br>classes is returned. Please note that breaking ties comes at a<br>relatively high computational cost compared to a simple predict. See<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_tie_breaking.py` for an<br>example of its usage with ``decision_function_shape='ovr'``.<br><br>.. versionadded:: 0.22</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.svm.SVC.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 pseudo random number generation for shuffling the data for<br>probability estimates. Ignored when `probability` is False.<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>
    
                      </tbody>
                    </table>
                </details>
            </div>
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    /**
     * 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-8');</script></body>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 136-144

New SequentialFeatureSelector transformer
-----------------------------------------
A new iterative transformer to select features is available:
:class:`~sklearn.feature_selection.SequentialFeatureSelector`.
Sequential Feature Selection can add features one at a time (forward
selection) or remove features from the list of the available features
(backward selection), based on a cross-validated score maximization.
See the :ref:`User Guide <sequential_feature_selection>`.

.. GENERATED FROM PYTHON SOURCE LINES 144-159

.. code-block:: Python


    from sklearn.datasets import load_iris
    from sklearn.feature_selection import SequentialFeatureSelector
    from sklearn.neighbors import KNeighborsClassifier

    X, y = load_iris(return_X_y=True, as_frame=True)
    feature_names = X.columns
    knn = KNeighborsClassifier(n_neighbors=3)
    sfs = SequentialFeatureSelector(knn, n_features_to_select=2)
    sfs.fit(X, y)
    print(
        "Features selected by forward sequential selection: "
        f"{feature_names[sfs.get_support()].tolist()}"
    )





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

 .. code-block:: none

    Features selected by forward sequential selection: ['sepal length (cm)', 'petal width (cm)']




.. GENERATED FROM PYTHON SOURCE LINES 160-166

New PolynomialCountSketch kernel approximation function
-------------------------------------------------------
The new :class:`~sklearn.kernel_approximation.PolynomialCountSketch`
approximates a polynomial expansion of a feature space when used with linear
models, but uses much less memory than
:class:`~sklearn.preprocessing.PolynomialFeatures`.

.. GENERATED FROM PYTHON SOURCE LINES 166-185

.. code-block:: Python


    from sklearn.datasets import fetch_covtype
    from sklearn.kernel_approximation import PolynomialCountSketch
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import MinMaxScaler

    X, y = fetch_covtype(return_X_y=True)
    pipe = make_pipeline(
        MinMaxScaler(),
        PolynomialCountSketch(degree=2, n_components=300),
        LogisticRegression(max_iter=1000),
    )
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, train_size=5000, test_size=10000, random_state=42
    )
    pipe.fit(X_train, y_train).score(X_test, y_test)





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

 .. code-block:: none


    0.7367



.. GENERATED FROM PYTHON SOURCE LINES 186-187

For comparison, here is the score of a linear baseline for the same data:

.. GENERATED FROM PYTHON SOURCE LINES 187-191

.. code-block:: Python


    linear_baseline = make_pipeline(MinMaxScaler(), LogisticRegression(max_iter=1000))
    linear_baseline.fit(X_train, y_train).score(X_test, y_test)





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

 .. code-block:: none


    0.7141



.. GENERATED FROM PYTHON SOURCE LINES 192-198

Individual Conditional Expectation plots
----------------------------------------
A new kind of partial dependence plot is available: the Individual
Conditional Expectation (ICE) plot. ICE plots visualize the dependence of the
prediction on a feature for each sample separately, with one line per sample.
See the :ref:`User Guide <individual_conditional>`

.. GENERATED FROM PYTHON SOURCE LINES 198-229

.. code-block:: Python


    from sklearn.datasets import fetch_california_housing
    from sklearn.ensemble import RandomForestRegressor

    # from sklearn.inspection import plot_partial_dependence
    from sklearn.inspection import PartialDependenceDisplay

    X, y = fetch_california_housing(return_X_y=True, as_frame=True)
    features = ["MedInc", "AveOccup", "HouseAge", "AveRooms"]
    est = RandomForestRegressor(n_estimators=10)
    est.fit(X, y)

    # plot_partial_dependence has been removed in version 1.2. From 1.2, use
    # PartialDependenceDisplay instead.
    # display = plot_partial_dependence(
    display = PartialDependenceDisplay.from_estimator(
        est,
        X,
        features,
        kind="individual",
        subsample=50,
        n_jobs=3,
        grid_resolution=20,
        random_state=0,
    )
    display.figure_.suptitle(
        "Partial dependence of house value on non-location features\n"
        "for the California housing dataset, with BayesianRidge"
    )
    display.figure_.subplots_adjust(hspace=0.3)




.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_24_0_001.png
   :alt: Partial dependence of house value on non-location features for the California housing dataset, with BayesianRidge
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_24_0_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 230-236

New Poisson splitting criterion for DecisionTreeRegressor
---------------------------------------------------------
The integration of Poisson regression estimation continues from version 0.23.
:class:`~sklearn.tree.DecisionTreeRegressor` now supports a new `'poisson'`
splitting criterion. Setting `criterion="poisson"` might be a good choice
if your target is a count or a frequency.

.. GENERATED FROM PYTHON SOURCE LINES 236-251

.. code-block:: Python


    import numpy as np

    from sklearn.model_selection import train_test_split
    from sklearn.tree import DecisionTreeRegressor

    n_samples, n_features = 1000, 20
    rng = np.random.RandomState(0)
    X = rng.randn(n_samples, n_features)
    # positive integer target correlated with X[:, 5] with many zeros:
    y = rng.poisson(lam=np.exp(X[:, 5]) / 2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
    regressor = DecisionTreeRegressor(criterion="poisson", random_state=0)
    regressor.fit(X_train, y_train)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-9 {
      /* 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-9.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-9.dark {
      --sklearn-color-text-on-default-background: white;
      --sklearn-color-background: #111;
      --sklearn-color-border-box: white;
      --sklearn-color-icon: #878787;
    }

    #sk-container-id-9 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-9 pre {
      padding: 0;
    }

    #sk-container-id-9 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-9 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-9 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-9 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-9 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-9 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-9 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-9 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-9 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-9 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-9 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-9 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-9 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-9 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-9 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-9 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-9 div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-9 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-9 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-9 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-9 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-9 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-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-9 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-9 div.sk-label label.sk-toggleable__label,
    #sk-container-id-9 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-9 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-9 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-9 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      line-height: 1.2em;
    }

    #sk-container-id-9 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-9 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-9 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-9 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-9 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-9 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-9 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-9 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-9 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;
    }

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    </style><body><div id="sk-container-id-9" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeRegressor(criterion=&#x27;poisson&#x27;, 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"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" checked><label for="sk-estimator-id-25" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>DecisionTreeRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html">?<span>Documentation for DecisionTreeRegressor</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">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="user-set">
                <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.tree.DecisionTreeRegressor.html#:~:text=criterion,-%7B%22squared_error%22%2C%20%22friedman_mse%22%2C%20%22absolute_error%22%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%22poisson%22%7D%2C%20default%3D%22squared_error%22">
                criterion
                <span class="param-doc-description">criterion: {"squared_error", "friedman_mse", "absolute_error",             "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 the half mean Poisson deviance to find splits.<br><br>.. versionadded:: 0.18<br>   Mean Absolute Error (MAE) criterion.<br><br>.. versionadded:: 0.24<br>    Poisson deviance criterion.</span>
            </a>
        </td>
                <td class="value">&#x27;poisson&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('splitter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
            <a class="param-doc-link"
                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22">
                splitter
                <span class="param-doc-description">splitter: {"best", "random"}, default="best"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are "best" to choose the best split and "random" to choose<br>the best random split.</span>
            </a>
        </td>
                <td class="value">&#x27;best&#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.tree.DecisionTreeRegressor.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.<br><br>For an example of how ``max_depth`` influences the model, see<br>:ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`.</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.tree.DecisionTreeRegressor.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.tree.DecisionTreeRegressor.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.tree.DecisionTreeRegressor.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.tree.DecisionTreeRegressor.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone">
                max_features
                <span class="param-doc-description">max_features: int, float or {"sqrt", "log2"}, default=None<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, then `max_features=n_features`.<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="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.tree.DecisionTreeRegressor.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 randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``"best"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>
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        </td>
                <td class="value">0</td>
            </tr>
    

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                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_leaf_nodes',
                              this.parentElement.nextElementSibling)"
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                <td class="param">
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                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.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 a tree 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.tree.DecisionTreeRegressor.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>
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                <td class="value">0.0</td>
            </tr>
    

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                <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.tree.DecisionTreeRegressor.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>
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                <td class="value">0.0</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('monotonic_cst',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">
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                rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.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: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<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>
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.. GENERATED FROM PYTHON SOURCE LINES 252-268

New documentation improvements
------------------------------

New examples and documentation pages have been added, in a continuous effort
to improve the understanding of machine learning practices:

- a new section about :ref:`common pitfalls and recommended
  practices <common_pitfalls>`,
- an example illustrating how to :ref:`statistically compare the performance of
  models <sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py>`
  evaluated using :class:`~sklearn.model_selection.GridSearchCV`,
- an example on how to :ref:`interpret coefficients of linear models
  <sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py>`,
- an :ref:`example
  <sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py>`
  comparing Principal Component Regression and Partial Least Squares.


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

   **Total running time of the script:** (0 minutes 15.967 seconds)


.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_24_0.py:

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