
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/miscellaneous/plot_partial_dependence_visualization_api.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_miscellaneous_plot_partial_dependence_visualization_api.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py:


=========================================
Advanced Plotting With Partial Dependence
=========================================
The :class:`~sklearn.inspection.PartialDependenceDisplay` object can be used
for plotting without needing to recalculate the partial dependence. In this
example, we show how to plot partial dependence plots and how to quickly
customize the plot with the visualization API.

.. note::

    See also :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`

.. GENERATED FROM PYTHON SOURCE LINES 15-29

.. code-block:: Python


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

    import matplotlib.pyplot as plt
    import pandas as pd

    from sklearn.datasets import load_diabetes
    from sklearn.inspection import PartialDependenceDisplay
    from sklearn.neural_network import MLPRegressor
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.tree import DecisionTreeRegressor








.. GENERATED FROM PYTHON SOURCE LINES 30-35

Train models on the diabetes dataset
================================================

First, we train a decision tree and a multi-layer perceptron on the diabetes
dataset.

.. GENERATED FROM PYTHON SOURCE LINES 35-48

.. code-block:: Python


    diabetes = load_diabetes()
    X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
    y = diabetes.target

    tree = DecisionTreeRegressor()
    mlp = make_pipeline(
        StandardScaler(),
        MLPRegressor(hidden_layer_sizes=(100, 100), tol=1e-2, max_iter=500, random_state=0),
    )
    tree.fit(X, y)
    mlp.fit(X, y)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-23 {
      /* 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;

      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-icon: #696969;

      @media (prefers-color-scheme: dark) {
        /* Redefinition of color scheme for dark theme */
        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-icon: #878787;
      }
    }

    #sk-container-id-23 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-23 pre {
      padding: 0;
    }

    #sk-container-id-23 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-23 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-23 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-23 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-23 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-23 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-23 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-23 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-23 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-23 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-23 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-23 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-23 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: start;
      justify-content: space-between;
      gap: 0.5em;
    }

    #sk-container-id-23 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-23 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-23 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-23 div.sk-toggleable__content {
      display: none;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

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

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

    #sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      display: block;
      width: 100%;
      overflow: visible;
    }

    #sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-23 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-23 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-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

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

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

    /* Estimator label */

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

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

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

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

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

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

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

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

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      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-1) 1pt solid;
      color: var(--sklearn-color-unfitted-level-1);
    }

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

    /* 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);
      color: var(--sklearn-color-background);
      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);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

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

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

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

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

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

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

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

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

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

    .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;
    }

    .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);
    }

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

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

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

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

    .copy-paste-icon {
        background-image: url(data:image/svg+xml;base64,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);
        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
        display: inline-block;
        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-23" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),
                    (&#x27;mlpregressor&#x27;,
                     MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
                                  random_state=0, tol=0.01))])</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-64" type="checkbox" ><label for="sk-estimator-id-64" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</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('steps',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">steps&nbsp;</td>
                <td class="value">[(&#x27;standardscaler&#x27;, ...), (&#x27;mlpregressor&#x27;, ...)]</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('transform_input',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">transform_input&nbsp;</td>
                <td class="value">None</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('memory',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">memory&nbsp;</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">verbose&nbsp;</td>
                <td class="value">False</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </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-65" type="checkbox" ><label for="sk-estimator-id-65" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted" data-param-prefix="standardscaler__">
            <div class="estimator-table">
                <details>
                    <summary>Parameters</summary>
                    <table class="parameters-table">
                      <tbody>
                    
            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('copy',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">copy&nbsp;</td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_mean',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">with_mean&nbsp;</td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('with_std',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">with_std&nbsp;</td>
                <td class="value">True</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-66" type="checkbox" ><label for="sk-estimator-id-66" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>MLPRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.neural_network.MLPRegressor.html">?<span>Documentation for MLPRegressor</span></a></div></label><div class="sk-toggleable__content fitted" data-param-prefix="mlpregressor__">
            <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">loss&nbsp;</td>
                <td class="value">&#x27;squared_error&#x27;</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('hidden_layer_sizes',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">hidden_layer_sizes&nbsp;</td>
                <td class="value">(100, ...)</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('activation',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">activation&nbsp;</td>
                <td class="value">&#x27;relu&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('solver',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">solver&nbsp;</td>
                <td class="value">&#x27;adam&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('alpha',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">alpha&nbsp;</td>
                <td class="value">0.0001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('batch_size',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">batch_size&nbsp;</td>
                <td class="value">&#x27;auto&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('learning_rate',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">learning_rate&nbsp;</td>
                <td class="value">&#x27;constant&#x27;</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('learning_rate_init',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">learning_rate_init&nbsp;</td>
                <td class="value">0.001</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('power_t',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">power_t&nbsp;</td>
                <td class="value">0.5</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_iter',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">max_iter&nbsp;</td>
                <td class="value">500</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('shuffle',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">shuffle&nbsp;</td>
                <td class="value">True</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('random_state',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">random_state&nbsp;</td>
                <td class="value">0</td>
            </tr>
    

            <tr class="user-set">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('tol',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">tol&nbsp;</td>
                <td class="value">0.01</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('verbose',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">verbose&nbsp;</td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('warm_start',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">warm_start&nbsp;</td>
                <td class="value">False</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('momentum',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">momentum&nbsp;</td>
                <td class="value">0.9</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('nesterovs_momentum',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">nesterovs_momentum&nbsp;</td>
                <td class="value">True</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('early_stopping',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">early_stopping&nbsp;</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">validation_fraction&nbsp;</td>
                <td class="value">0.1</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('beta_1',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">beta_1&nbsp;</td>
                <td class="value">0.9</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('beta_2',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">beta_2&nbsp;</td>
                <td class="value">0.999</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('epsilon',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">epsilon&nbsp;</td>
                <td class="value">1e-08</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">n_iter_no_change&nbsp;</td>
                <td class="value">10</td>
            </tr>
    

            <tr class="default">
                <td><i class="copy-paste-icon"
                     onclick="copyToClipboard('max_fun',
                              this.parentElement.nextElementSibling)"
                ></i></td>
                <td class="param">max_fun&nbsp;</td>
                <td class="value">15000</td>
            </tr>
    
                      </tbody>
                    </table>
                </details>
            </div>
        </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {
        // Get the parameter prefix from the closest toggleable content
        const toggleableContent = element.closest('.sk-toggleable__content');
        const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
        const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;

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

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

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

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

        element.setAttribute('title', fullParamName);
    });
    </script></body>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-57

Plotting partial dependence for two features
============================================

We plot partial dependence curves for features "age" and "bmi" (body mass
index) for the decision tree. With two features,
:func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` expects to plot
two curves. Here the plot function place a grid of two plots using the space
defined by `ax` .

.. GENERATED FROM PYTHON SOURCE LINES 57-61

.. code-block:: Python

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.set_title("Decision Tree")
    tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age", "bmi"], ax=ax)




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_001.png
   :alt: Decision Tree
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 62-66

The partial dependence curves can be plotted for the multi-layer perceptron.
In this case, `line_kw` is passed to
:func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` to change the
color of the curve.

.. GENERATED FROM PYTHON SOURCE LINES 66-72

.. code-block:: Python

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.set_title("Multi-layer Perceptron")
    mlp_disp = PartialDependenceDisplay.from_estimator(
        mlp, X, ["age", "bmi"], ax=ax, line_kw={"color": "red"}
    )




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_002.png
   :alt: Multi-layer Perceptron
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 73-90

Plotting partial dependence of the two models together
======================================================

The `tree_disp` and `mlp_disp`
:class:`~sklearn.inspection.PartialDependenceDisplay` objects contain all the
computed information needed to recreate the partial dependence curves. This
means we can easily create additional plots without needing to recompute the
curves.

One way to plot the curves is to place them in the same figure, with the
curves of each model on each row. First, we create a figure with two axes
within two rows and one column. The two axes are passed to the
:func:`~sklearn.inspection.PartialDependenceDisplay.plot` functions of
`tree_disp` and `mlp_disp`. The given axes will be used by the plotting
function to draw the partial dependence. The resulting plot places the
decision tree partial dependence curves in the first row of the
multi-layer perceptron in the second row.

.. GENERATED FROM PYTHON SOURCE LINES 90-97

.. code-block:: Python


    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))
    tree_disp.plot(ax=ax1)
    ax1.set_title("Decision Tree")
    mlp_disp.plot(ax=ax2, line_kw={"color": "red"})
    ax2.set_title("Multi-layer Perceptron")




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_003.png
   :alt: Decision Tree, Multi-layer Perceptron
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_003.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Text(0.5, 1.0, 'Multi-layer Perceptron')



.. GENERATED FROM PYTHON SOURCE LINES 98-103

Another way to compare the curves is to plot them on top of each other. Here,
we create a figure with one row and two columns. The axes are passed into the
:func:`~sklearn.inspection.PartialDependenceDisplay.plot` function as a list,
which will plot the partial dependence curves of each model on the same axes.
The length of the axes list must be equal to the number of plots drawn.

.. GENERATED FROM PYTHON SOURCE LINES 103-112

.. code-block:: Python


    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
    tree_disp.plot(ax=[ax1, ax2], line_kw={"label": "Decision Tree"})
    mlp_disp.plot(
        ax=[ax1, ax2], line_kw={"label": "Multi-layer Perceptron", "color": "red"}
    )
    ax1.legend()
    ax2.legend()




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_004.png
   :alt: plot partial dependence visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <matplotlib.legend.Legend object at 0x7877b1e4c590>



.. GENERATED FROM PYTHON SOURCE LINES 114-120

`tree_disp.axes_` is a numpy array container the axes used to draw the
partial dependence plots. This can be passed to `mlp_disp` to have the same
affect of drawing the plots on top of each other. Furthermore, the
`mlp_disp.figure_` stores the figure, which allows for resizing the figure
after calling `plot`. In this case `tree_disp.axes_` has two dimensions, thus
`plot` will only show the y label and y ticks on the left most plot.

.. GENERATED FROM PYTHON SOURCE LINES 120-130

.. code-block:: Python


    tree_disp.plot(line_kw={"label": "Decision Tree"})
    mlp_disp.plot(
        line_kw={"label": "Multi-layer Perceptron", "color": "red"}, ax=tree_disp.axes_
    )
    tree_disp.figure_.set_size_inches(10, 6)
    tree_disp.axes_[0, 0].legend()
    tree_disp.axes_[0, 1].legend()
    plt.show()




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_005.png
   :alt: plot partial dependence visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 131-137

Plotting partial dependence for one feature
===========================================

Here, we plot the partial dependence curves for a single feature, "age", on
the same axes. In this case, `tree_disp.axes_` is passed into the second
plot function.

.. GENERATED FROM PYTHON SOURCE LINES 137-141

.. code-block:: Python

    tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age"])
    mlp_disp = PartialDependenceDisplay.from_estimator(
        mlp, X, ["age"], ax=tree_disp.axes_, line_kw={"color": "red"}
    )



.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_006.png
   :alt: plot partial dependence visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_006.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 5.952 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py:

.. only:: html

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

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

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

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

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

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

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


.. include:: plot_partial_dependence_visualization_api.recommendations


.. only:: html

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

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