.. 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

        :ref:`Go to the end <sphx_glr_download_auto_examples_neighbors_plot_classification.py>`
        to download the full example code or to run this example in your browser via JupyterLite or 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 10-63

.. code-block:: default


    import matplotlib.pyplot as plt
    import seaborn as sns
    from matplotlib.colors import ListedColormap
    from sklearn import neighbors, datasets
    from sklearn.inspection import DecisionBoundaryDisplay

    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

    # 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)

        _, ax = plt.subplots()
        DecisionBoundaryDisplay.from_estimator(
            clf,
            X,
            cmap=cmap_light,
            ax=ax,
            response_method="predict",
            plot_method="pcolormesh",
            xlabel=iris.feature_names[0],
            ylabel=iris.feature_names[1],
            shading="auto",
        )

        # 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.title(
            "3-Class classification (k = %i, weights = '%s')" % (n_neighbors, weights)
        )

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_classification.py:

.. only:: html

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


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_classification.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/neighbors/plot_classification.ipynb
        :alt: Launch JupyterLite
        :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>`


.. only:: html

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

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