Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights.
1 2 3 4 5 6 | print(__doc__)# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr># Fabian Pedregosa <fabian.pedregosa@inria.fr>## License: BSD 3 clause (C) INRIA |
Generate sample data
1 2 3 4 5 6 7 8 9 10 11 | import numpy as npimport matplotlib.pyplot as pltfrom sklearn import neighborsnp.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 targetsy[::5] += 1 * (0.5 - np.random.rand(8)) |
Fit regression model
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | n_neighbors = 5for 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() |
Total running time of the script: (0 minutes 0.119 seconds)
Download Python source code:
plot_regression.py
Download IPython notebook:
plot_regression.ipynb
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