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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_packages_scikit-learn_auto_examples_plot_iris_knn.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_packages_scikit-learn_auto_examples_plot_iris_knn.py:


Nearest-neighbor prediction on iris
====================================

Plot the decision boundary of nearest neighbor decision on iris, first
with a single nearest neighbor, and then using 3 nearest neighbors.

.. GENERATED FROM PYTHON SOURCE LINES 8-33

.. code-block:: default


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

    # Create color maps for 3-class classification problem, as with iris
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

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

    knn = neighbors.KNeighborsClassifier(n_neighbors=1)
    knn.fit(X, y)

    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.linspace(x_min, x_max, 100),
                            np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])









.. GENERATED FROM PYTHON SOURCE LINES 34-35

Put the result into a color plot

.. GENERATED FROM PYTHON SOURCE LINES 35-46

.. code-block:: default

    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)
    plt.xlabel('sepal length (cm)')
    plt.ylabel('sepal width (cm)')
    plt.axis('tight')





.. image-sg:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_iris_knn_001.png
   :alt: plot iris knn
   :srcset: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_iris_knn_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (4.180808080808081, 8.019191919191918, 1.8868686868686868, 4.513131313131313)



.. GENERATED FROM PYTHON SOURCE LINES 47-48

And now, redo the analysis with 3 neighbors

.. GENERATED FROM PYTHON SOURCE LINES 48-65

.. code-block:: default

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    Z = knn.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)
    plt.xlabel('sepal length (cm)')
    plt.ylabel('sepal width (cm)')
    plt.axis('tight')

    plt.show()



.. image-sg:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_iris_knn_002.png
   :alt: plot iris knn
   :srcset: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_iris_knn_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.396 seconds)


.. _sphx_glr_download_packages_scikit-learn_auto_examples_plot_iris_knn.py:

.. only:: html

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




    .. container:: sphx-glr-download sphx-glr-download-python

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

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

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


.. only:: html

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

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