RidgeClassifierCV

• Implementa un clasificador usando regresión Ridge con validación cruzada.

• Primero convierte y en \{-1, 1\}, y luego resuelve el problema usando regresión.

• Por defecto usa leave-one-out cross-validation.

from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True)

from sklearn.linear_model import RidgeClassifierCV

ridgeClassifierCV = RidgeClassifierCV(
    # ---------------------------------------------------------------------
    # Regularization strength; must be a positive float. Regularization
    # improves the conditioning of the problem and reduces the variance of
    # the estimates. Larger values specify stronger regularization. Alpha
    # corresponds to 1 / (2C) in other linear models such as
    # LogisticRegression or LinearSVC.
    # alphas=(0.1, 1.0, 10.0),
    alphas=[1e-3, 1e-2, 1e-1, 1],
    # ---------------------------------------------------------------------
    # Whether to fit the intercept for this model.
    fit_intercept=True,
    # ---------------------------------------------------------------------
    # A string (see model evaluation documentation) or a scorer callable
    # object / function with signature scorer(estimator, X, y).
    scoring=None,
    # ---------------------------------------------------------------------
    # Determines the cross-validation splitting strategy. Possible inputs
    # for cv are:
    # * None, to use the efficient Leave-One-Out cross-validation
    # * integer, to specify the number of folds.
    # * CV splitter
    # * An iterable yielding (train, test) splits as arrays of indices.
    cv=None,
    # ---------------------------------------------------------------------
    # Weights associated with classes in the form {class_label: weight}
    class_weight=None,
    # ---------------------------------------------------------------------
    # Flag indicating if the cross-validation values corresponding to each
    # alpha should be stored in the cv_values_ attribute (see below). This
    # flag is only compatible with cv=None (i.e. using Leave-One-Out
    # Cross-Validation).
    store_cv_values=False,
)

ridgeClassifierCV.fit(X, y)

ridgeClassifierCV.score(X, y)

0.9630931458699473

ridgeClassifierCV.alpha_

0.01

ridgeClassifierCV.coef_

array([[ 2.70962810e-01, -1.99904473e-02, -2.68869779e-02,
        -5.52130776e-04,  7.51784333e-01,  6.70081562e+00,
        -2.24514478e+00, -3.78660924e+00, -2.34922271e-01,
         3.05972457e-01, -9.50391185e-01, -3.25337487e-02,
         7.39111510e-03,  3.34830382e-03, -5.15606649e+00,
        -1.39236707e+00,  4.94530386e+00, -4.00371830e+00,
        -2.54634379e+00, -1.35630031e-02, -3.49442568e-01,
        -5.24646097e-03,  7.11490969e-03,  1.75379598e-03,
        -4.16225726e+00, -5.32987896e-02, -6.21848378e-01,
        -2.48894530e+00, -9.96733111e-01, -4.46615638e+00]])

ridgeClassifierCV.intercept_

array([4.76062228])