.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_plot_roc_curve_visualization_api.py>` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_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.


.. code-block:: default

    print(__doc__)








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.


.. code-block:: default

    import matplotlib.pyplot as plt
    from sklearn.svm import SVC
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import plot_roc_curve
    from sklearn.datasets import load_wine
    from sklearn.model_selection import train_test_split

    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)





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

 Out:

 .. code-block:: none


    SVC(random_state=42)



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


.. code-block:: default

    svc_disp = plot_roc_curve(svc, X_test, y_test)
    plt.show()




.. image:: /auto_examples/images/sphx_glr_plot_roc_curve_visualization_api_001.png
    :class: sphx-glr-single-img





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.


.. code-block:: default

    rfc = RandomForestClassifier(n_estimators=10, random_state=42)
    rfc.fit(X_train, y_train)
    ax = plt.gca()
    rfc_disp = plot_roc_curve(rfc, X_test, y_test, ax=ax, alpha=0.8)
    svc_disp.plot(ax=ax, alpha=0.8)
    plt.show()



.. image:: /auto_examples/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  0.567 seconds)

**Estimated memory usage:**  12 MB


.. _sphx_glr_download_auto_examples_plot_roc_curve_visualization_api.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/plot_roc_curve_visualization_api.ipynb
      :width: 150 px


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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