.. 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 13-17

Load Data and Train a 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 17-32

.. 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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         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 */
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    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
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      float: right;
      font-size: 1rem;
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      width: 1rem;
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      /* unfitted */
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    #sk-container-id-52 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-52" 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-224" type="checkbox" checked><label for="sk-estimator-id-224" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;SVC<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html">?<span>Documentation for SVC</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>SVC(random_state=42)</pre></div> </div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 33-39

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 39-42

.. 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 43-51

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 51-57

.. 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(rfc, X_test, y_test, ax=ax, alpha=0.8)
    svc_disp.plot(ax=ax, 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

   **Total running time of the script:** (0 minutes 0.188 seconds)


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