
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/miscellaneous/plot_roc_curve_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_roc_curve_visualization_api.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_miscellaneous_plot_roc_curve_visualization_api.py:


================================
ROC Curve with Visualization API
================================
Scikit-learn defines a simple API for creating visualizations for machine
learning. The key features of this API is to allow for quick plotting and
visual adjustments without recalculation. In this example, we will demonstrate
how to use the visualization API by comparing ROC curves.

.. GENERATED FROM PYTHON SOURCE LINES 11-15

.. code-block:: Python


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








.. GENERATED FROM PYTHON SOURCE LINES 16-20

Load Data and Train an SVC
--------------------------
First, we load the wine dataset and convert it to a binary classification
problem. Then, we train a support vector classifier on a training dataset.

.. GENERATED FROM PYTHON SOURCE LINES 20-35

.. code-block:: Python

    import matplotlib.pyplot as plt

    from sklearn.datasets import load_wine
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import RocCurveDisplay
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC

    X, y = load_wine(return_X_y=True)
    y = y == 2

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    svc = SVC(random_state=42)
    svc.fit(X_train, y_train)






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      --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;
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      /* unfitted */
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    .sk-estimator-doc-link.fitted,
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      background-color: var(--sklearn-color-fitted-level-0);
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      display: none;
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      font-weight: normal;
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      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
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      border: .5pt solid var(--sklearn-color-unfitted-level-3);
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    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
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        background-repeat: no-repeat;
        background-size: 14px 14px;
        background-position: 0;
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        width: 14px;
        height: 14px;
        cursor: pointer;
    }
    </style><body><div id="sk-container-id-62" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-274" type="checkbox" checked><label for="sk-estimator-id-274" 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><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="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">
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            <a class="param-doc-link"
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                <td class="value">True</td>
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                <td class="value">False</td>
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                <td class="value">-1</td>
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                              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.svm.SVC.html#:~:text=decision_function_shape,-%7B%27ovo%27%2C%20%27ovr%27%7D%2C%20default%3D%27ovr%27">
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                     onclick="copyToClipboard('break_ties',
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                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>
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                <td class="value">42</td>
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.. GENERATED FROM PYTHON SOURCE LINES 36-42

Plotting the ROC Curve
----------------------
Next, we plot the ROC curve with a single call to
:func:`sklearn.metrics.RocCurveDisplay.from_estimator`. The returned
`svc_disp` object allows us to continue using the already computed ROC curve
for the SVC in future plots.

.. GENERATED FROM PYTHON SOURCE LINES 42-45

.. code-block:: Python

    svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
    plt.show()




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png
   :alt: plot roc curve visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 46-54

Training a Random Forest and Plotting the ROC Curve
---------------------------------------------------
We train a random forest classifier and create a plot comparing it to the SVC
ROC curve. Notice how `svc_disp` uses
:func:`~sklearn.metrics.RocCurveDisplay.plot` to plot the SVC ROC curve
without recomputing the values of the roc curve itself. Furthermore, we
pass `alpha=0.8` to the plot functions to adjust the alpha values of the
curves.

.. GENERATED FROM PYTHON SOURCE LINES 54-62

.. code-block:: Python

    rfc = RandomForestClassifier(n_estimators=10, random_state=42)
    rfc.fit(X_train, y_train)
    ax = plt.gca()
    rfc_disp = RocCurveDisplay.from_estimator(
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    )
    svc_disp.plot(ax=ax, curve_kwargs=dict(alpha=0.8))
    plt.show()



.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png
   :alt: plot roc curve visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_roc_curve_visualization_api_002.png
   :class: sphx-glr-single-img






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

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