.. 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

        Click :ref:`here <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 10-72



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


    *

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

    *

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


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

 Out:

 .. code-block:: none


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






|

.. code-block:: default

    print(__doc__)
    # Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
    # License: BSD 3 clause

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.datasets import make_blobs
    from sklearn.linear_model import LogisticRegression

    # 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,
                                 multi_class=multi_class).fit(X, y)

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

        # create a mesh to plot in
        h = .02  # step size in the mesh
        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                             np.arange(y_min, y_max, h))

        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, x_max]x[y_min, y_max].
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.figure()
        plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
        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, cmap=plt.cm.Paired,
                        edgecolor='black', s=20)

        # Plot the three one-against-all classifiers
        xmin, xmax = plt.xlim()
        ymin, ymax = plt.ylim()
        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  0.347 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_multinomial.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?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-python

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



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

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


.. only:: html

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

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