LarsCV

• Implementa un modelo LARS con validación cruzada.

from sklearn.datasets import load_diabetes

X, y = load_diabetes(return_X_y=True)

from sklearn.linear_model import LarsCV

larsCV = LarsCV(
    # ---------------------------------------------------------------------
    # Whether to fit the intercept for this model.
    fit_intercept=True,
    # --------------------------------------------------------------------------
    # The maximum number of iterations.
    max_iter=500,
    # --------------------------------------------------------------------------
    # Determines the cross-validation splitting strategy. Possible inputs for
    # cv are:
    # * None, to use the default 5-fold cross-validation,
    # * int, to specify the number of folds.
    # * CV splitter,
    # * An iterable yielding (train, test) splits as arrays of indices.
    cv=None,
    # --------------------------------------------------------------------------
    # The maximum number of points on the path used to compute the residuals in
    # the cross-validation.
    max_n_alphas=1000,
)

larsCV.fit(X, y)

larsCV.score(X, y)

0.5000524336939126

larsCV.alpha_

0.16079438841644006

larsCV.coef_

array([   0.        , -108.03748015,  511.97451874,  250.57032169,
          0.        ,    0.        , -193.2451994 ,    0.        ,
        452.20797343,   10.79581283])

larsCV.intercept_

152.13348416289602