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

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

.. _sphx_glr_auto_examples_neighbors_plot_regression.py:


============================
Nearest Neighbors regression
============================

Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.




.. code-block:: python

    print(__doc__)

    # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
    #         Fabian Pedregosa <fabian.pedregosa@inria.fr>
    #
    # License: BSD 3 clause (C) INRIA


    # #############################################################################
    # Generate sample data
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import neighbors

    np.random.seed(0)
    X = np.sort(5 * np.random.rand(40, 1), axis=0)
    T = np.linspace(0, 5, 500)[:, np.newaxis]
    y = np.sin(X).ravel()

    # Add noise to targets
    y[::5] += 1 * (0.5 - np.random.rand(8))

    # #############################################################################
    # Fit regression model
    n_neighbors = 5

    for i, weights in enumerate(['uniform', 'distance']):
        knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
        y_ = knn.fit(X, y).predict(T)

        plt.subplot(2, 1, i + 1)
        plt.scatter(X, y, c='k', label='data')
        plt.plot(T, y_, c='g', label='prediction')
        plt.axis('tight')
        plt.legend()
        plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors,
                                                                    weights))

    plt.tight_layout()
    plt.show()

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


.. _sphx_glr_download_auto_examples_neighbors_plot_regression.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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