Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector, and this is displayed as a function of the regularization parameter.
Out:
Computing regularization path using the LARS ... .
print(__doc__) # Author: Fabian Pedregosa <fabian.pedregosa@inria.fr> # Alexandre Gramfort <alexandre.gramfort@inria.fr> # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model from sklearn import datasets diabetes = datasets.load_diabetes() X = diabetes.data y = diabetes.target print("Computing regularization path using the LARS ...") alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True) xx = np.sum(np.abs(coefs.T), axis=1) xx /= xx[-1] plt.plot(xx, coefs.T) ymin, ymax = plt.ylim() plt.vlines(xx, ymin, ymax, linestyle='dashed') plt.xlabel('|coef| / max|coef|') plt.ylabel('Coefficients') plt.title('LASSO Path') plt.axis('tight') plt.show()
Total running time of the script: (0 minutes 0.107 seconds)
Download Python source code:
plot_lasso_lars.py
Download IPython notebook:
plot_lasso_lars.ipynb
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