.. 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 ` 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 # Fabian Pedregosa # # 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 0.218 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/1.4.X?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 ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_regression.py ` .. include:: plot_regression.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_