Lasso path using LARS — scikit-learn 0.20.4 documentation (original) (raw)
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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 ...") _, _, 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 0.162 seconds)