.. _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 # Fabian Pedregosa # # License: BSD 3 clause (C) INRIA Generate sample data .. code-block:: python 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 .. 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, 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.show() .. image:: /auto_examples/neighbors/images/sphx_glr_plot_regression_001.png :align: center **Total running time of the script:** (0 minutes 0.127 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_regression.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_regression.ipynb `