.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neighbors/plot_regression.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_regression.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_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.

.. GENERATED FROM PYTHON SOURCE LINES 11-18

.. code-block:: Python


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









.. GENERATED FROM PYTHON SOURCE LINES 19-21

Generate sample data
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-34

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np

    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))








.. GENERATED FROM PYTHON SOURCE LINES 35-37

Fit regression model
--------------------

.. GENERATED FROM PYTHON SOURCE LINES 37-52

.. code-block:: Python

    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, color="darkorange", label="data")
        plt.plot(T, y_, color="navy", label="prediction")
        plt.axis("tight")
        plt.legend()
        plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))

    plt.tight_layout()
    plt.show()



.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_regression_001.png
   :alt: KNeighborsRegressor (k = 5, weights = 'uniform'), KNeighborsRegressor (k = 5, weights = 'distance')
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_regression_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.228 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_regression.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_regression.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/neighbors/plot_regression.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_regression.recommendations


.. only:: html

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

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