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


===============================
Plot classification probability
===============================

Plot the classification probability for different classifiers. We use a 3 class
dataset, and we classify it with a Support Vector classifier, L1 and L2
penalized logistic regression with either a One-Vs-Rest or multinomial setting,
and Gaussian process classification.

Linear SVC is not a probabilistic classifier by default but it has a built-in
calibration option enabled in this example (`probability=True`).

The logistic regression with One-Vs-Rest is not a multiclass classifier out of
the box. As a result it has more trouble in separating class 2 and 3 than the
other estimators.

.. GENERATED FROM PYTHON SOURCE LINES 19-97



.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_classification_probability_001.png
   :alt: Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Probability
   :srcset: /auto_examples/classification/images/sphx_glr_plot_classification_probability_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Accuracy (train) for L1 logistic: 83.3%
    Accuracy (train) for L2 logistic (Multinomial): 82.7%
    Accuracy (train) for L2 logistic (OvR): 79.3%
    Accuracy (train) for Linear SVC: 82.0%
    Accuracy (train) for GPC: 82.7%






|

.. code-block:: Python


    # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np
    from matplotlib import cm

    from sklearn import datasets
    from sklearn.gaussian_process import GaussianProcessClassifier
    from sklearn.gaussian_process.kernels import RBF
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import accuracy_score
    from sklearn.svm import SVC

    iris = datasets.load_iris()
    X = iris.data[:, 0:2]  # we only take the first two features for visualization
    y = iris.target

    n_features = X.shape[1]

    C = 10
    kernel = 1.0 * RBF([1.0, 1.0])  # for GPC

    # Create different classifiers.
    classifiers = {
        "L1 logistic": LogisticRegression(
            C=C, penalty="l1", solver="saga", multi_class="multinomial", max_iter=10000
        ),
        "L2 logistic (Multinomial)": LogisticRegression(
            C=C, penalty="l2", solver="saga", multi_class="multinomial", max_iter=10000
        ),
        "L2 logistic (OvR)": LogisticRegression(
            C=C, penalty="l2", solver="saga", multi_class="ovr", max_iter=10000
        ),
        "Linear SVC": SVC(kernel="linear", C=C, probability=True, random_state=0),
        "GPC": GaussianProcessClassifier(kernel),
    }

    n_classifiers = len(classifiers)

    fig, axes = plt.subplots(
        nrows=n_classifiers,
        ncols=len(iris.target_names),
        figsize=(3 * 2, n_classifiers * 2),
    )
    for classifier_idx, (name, classifier) in enumerate(classifiers.items()):
        y_pred = classifier.fit(X, y).predict(X)
        accuracy = accuracy_score(y, y_pred)
        print(f"Accuracy (train) for {name}: {accuracy:0.1%}")
        for label in np.unique(y):
            # plot the probability estimate provided by the classifier
            disp = DecisionBoundaryDisplay.from_estimator(
                classifier,
                X,
                response_method="predict_proba",
                class_of_interest=label,
                ax=axes[classifier_idx, label],
                vmin=0,
                vmax=1,
            )
            axes[classifier_idx, label].set_title(f"Class {label}")
            # plot data predicted to belong to given class
            mask_y_pred = y_pred == label
            axes[classifier_idx, label].scatter(
                X[mask_y_pred, 0], X[mask_y_pred, 1], marker="o", c="w", edgecolor="k"
            )
            axes[classifier_idx, label].set(xticks=(), yticks=())
        axes[classifier_idx, 0].set_ylabel(name)

    ax = plt.axes([0.15, 0.04, 0.7, 0.02])
    plt.title("Probability")
    _ = plt.colorbar(
        cm.ScalarMappable(norm=None, cmap="viridis"), cax=ax, orientation="horizontal"
    )

    plt.show()


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

   **Total running time of the script:** (0 minutes 1.402 seconds)


.. _sphx_glr_download_auto_examples_classification_plot_classification_probability.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/classification/plot_classification_probability.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/classification/plot_classification_probability.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_classification_probability.recommendations


.. only:: html

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

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