
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neighbors/plot_classification.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. 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.

.. GENERATED FROM PYTHON SOURCE LINES 10-68



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


    *

      .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
         :alt: 3-Class classification (k = 15, weights = 'uniform')
         :srcset: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_classification_002.png
         :alt: 3-Class classification (k = 15, weights = 'distance')
         :srcset: /auto_examples/neighbors/images/sphx_glr_plot_classification_002.png
         :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    /usr/lib/python3/dist-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'rocket' which already exists.
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      mpl_cm.register_cmap(_name + "_r", _cmap_r)
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      mpl_cm.register_cmap(_name, _cmap)
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      mpl_cm.register_cmap(_name + "_r", _cmap_r)
    /usr/lib/python3/dist-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'flare' which already exists.
      mpl_cm.register_cmap(_name, _cmap)
    /usr/lib/python3/dist-packages/seaborn/cm.py:1583: UserWarning: Trying to register the cmap 'flare_r' which already exists.
      mpl_cm.register_cmap(_name + "_r", _cmap_r)
    /usr/lib/python3/dist-packages/seaborn/cm.py:1582: UserWarning: Trying to register the cmap 'crest' which already exists.
      mpl_cm.register_cmap(_name, _cmap)
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      mpl_cm.register_cmap(_name + "_r", _cmap_r)






|

.. code-block:: default


    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    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 = 0.02  # step size in the mesh

    # Create color maps
    cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"])
    cmap_bold = ["darkorange", "c", "darkblue"]

    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(figsize=(8, 6))
        plt.contourf(xx, yy, Z, cmap=cmap_light)

        # Plot also the training points
        sns.scatterplot(
            x=X[:, 0],
            y=X[:, 1],
            hue=iris.target_names[y],
            palette=cmap_bold,
            alpha=1.0,
            edgecolor="black",
        )
        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.xlabel(iris.feature_names[0])
        plt.ylabel(iris.feature_names[1])

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.485 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 sphx-glr-download-python

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



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

     :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.github.io>`_
