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

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

.. _sphx_glr_auto_examples_neighbors_plot_classification.py:


================================
Nearest Neighbors Classification
================================

Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.




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


    *

      .. image:: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
            :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/neighbors/images/sphx_glr_plot_classification_002.png
            :class: sphx-glr-multi-img





.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.colors import ListedColormap
    from sklearn import neighbors, datasets

    n_neighbors = 15

    # import some data to play with
    iris = datasets.load_iris()

    # we only take the first two features. We could avoid this ugly
    # slicing by using a two-dim dataset
    X = iris.data[:, :2]
    y = iris.target

    h = .02  # step size in the mesh

    # Create color maps
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

    for weights in ['uniform', 'distance']:
        # we create an instance of Neighbours Classifier and fit the data.
        clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
        clf.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() - 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))
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.figure()
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

        # Plot also the training points
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                    edgecolor='k', s=20)
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())
        plt.title("3-Class classification (k = %i, weights = '%s')"
                  % (n_neighbors, weights))

    plt.show()

**Total running time of the script:** ( 0 minutes  1.416 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_classification.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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