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scikit-learn 1.3.2
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# Lasso path using LARS

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.

    Computing regularization path using the LARS ...
    .

    # Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
    #         Alexandre Gramfort <alexandre.gramfort@inria.fr>
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import datasets, linear_model

    X, y = datasets.load_diabetes(return_X_y=True)

    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 0.053 seconds)

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