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


====================================================
Plot multinomial and One-vs-Rest Logistic Regression
====================================================

Plot decision surface of multinomial and One-vs-Rest Logistic Regression.
The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers
are represented by the dashed lines.

.. GENERATED FROM PYTHON SOURCE LINES 11-71



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_multinomial_001.png
         :alt: Decision surface of LogisticRegression (multinomial)
         :srcset: /auto_examples/linear_model/images/sphx_glr_plot_logistic_multinomial_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_multinomial_002.png
         :alt: Decision surface of LogisticRegression (ovr)
         :srcset: /auto_examples/linear_model/images/sphx_glr_plot_logistic_multinomial_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    training score : 0.995 (multinomial)
    training score : 0.976 (ovr)






|

.. code-block:: Python


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

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import make_blobs
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.linear_model import LogisticRegression
    from sklearn.multiclass import OneVsRestClassifier

    # make 3-class dataset for classification
    centers = [[-5, 0], [0, 1.5], [5, -1]]
    X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)
    transformation = [[0.4, 0.2], [-0.4, 1.2]]
    X = np.dot(X, transformation)

    for multi_class in ("multinomial", "ovr"):
        clf = LogisticRegression(solver="sag", max_iter=100, random_state=42)
        if multi_class == "ovr":
            clf = OneVsRestClassifier(clf)
        clf.fit(X, y)

        # print the training scores
        print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))

        _, ax = plt.subplots()
        DecisionBoundaryDisplay.from_estimator(
            clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
        )
        plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
        plt.axis("tight")

        # Plot also the training points
        colors = "bry"
        for i, color in zip(clf.classes_, colors):
            idx = np.where(y == i)
            plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20)

        # Plot the three one-against-all classifiers
        xmin, xmax = plt.xlim()
        ymin, ymax = plt.ylim()
        if multi_class == "ovr":
            coef = np.concatenate([est.coef_ for est in clf.estimators_])
            intercept = np.concatenate([est.intercept_ for est in clf.estimators_])
        else:
            coef = clf.coef_
            intercept = clf.intercept_

        def plot_hyperplane(c, color):
            def line(x0):
                return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]

            plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)

        for i, color in zip(clf.classes_, colors):
            plot_hyperplane(i, color)

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.166 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_multinomial.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/linear_model/plot_logistic_multinomial.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_logistic_multinomial.recommendations


.. only:: html

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

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