.. 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 or to run this example in your browser via Binder

.. 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 9-62



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


    *

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

    *

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





.. code-block:: default

    print(__doc__)

    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 = .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  2.010 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_classification.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/neighbors/plot_classification.ipynb
      :alt: Launch binder
      :width: 150 px


  .. 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>`


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