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

    Click :ref:`here <sphx_glr_download_auto_examples_linear_model_plot_iris_logistic.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_linear_model_plot_iris_logistic.py:


=========================================================
Logistic Regression 3-class Classifier
=========================================================

Show below is a logistic-regression classifiers decision boundaries on the
first two dimensions (sepal length and width) of the `iris
<https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The datapoints
are colored according to their labels.




.. image:: /auto_examples/linear_model/images/sphx_glr_plot_iris_logistic_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none







|


.. code-block:: default

    print(__doc__)

    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

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

    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    Y = iris.target

    logreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')

    # Create an instance of Logistic Regression Classifier and fit the data.
    logreg.fit(X, Y)

    # 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].
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = .02  # step size in the mesh
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure(1, figsize=(4, 3))
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')

    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.274 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_iris_logistic.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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