File: plot_regression.py

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scikit-learn 0.18-5
 `12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849` ``````""" ============================ 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. """ print(__doc__) # Author: Alexandre Gramfort # Fabian Pedregosa # # 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.show() ``````