# 3.2.4.1.10. sklearn.linear_model.RidgeClassifierCV¶

class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False)[source]

Ridge classifier with built-in cross-validation.

See glossary entry for cross-validation estimator.

By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation. Currently, only the n_features > n_samples case is handled efficiently.

Read more in the User Guide.

Parameters: alphas : numpy array of shape [n_alphas] Array of alpha values to try. 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 C^-1 in other linear models such as LogisticRegression or LinearSVC. fit_intercept : boolean Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False. scoring : string, callable or None, optional, default: None A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). cv : int, cross-validation generator or an iterable, optional 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. Refer User Guide for the various cross-validation strategies that can be used here. class_weight : dict or ‘balanced’, optional Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)) store_cv_values : boolean, default=False 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 Generalized Cross-Validation). cv_values_ : array, shape = [n_samples, n_targets, n_alphas], optional Cross-validation values for each alpha (if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor). coef_ : array, shape = [n_features] or [n_targets, n_features] Weight vector(s). intercept_ : float | array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if fit_intercept = False. alpha_ : float Estimated regularization parameter

Ridge
Ridge regression
RidgeClassifier
Ridge classifier
RidgeCV
Ridge regression with built-in cross validation

Notes

For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import RidgeClassifierCV
>>> clf = RidgeClassifierCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
>>> clf.score(X, y)
0.9630...


Methods

 decision_function(X) Predict confidence scores for samples. fit(X, y[, sample_weight]) Fit the ridge classifier. get_params([deep]) Get parameters for this estimator. predict(X) Predict class labels for samples in X. score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of this estimator.
__init__(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False)[source]
decision_function(X)[source]

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters: X : array_like or sparse matrix, shape (n_samples, n_features) Samples. array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
fit(X, y, sample_weight=None)[source]

Fit the ridge classifier.

Parameters: X : array-like, shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target values. Will be cast to X’s dtype if necessary sample_weight : float or numpy array of shape (n_samples,) Sample weight. self : object
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters: deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. params : mapping of string to any Parameter names mapped to their values.
predict(X)[source]

Predict class labels for samples in X.

Parameters: X : array_like or sparse matrix, shape (n_samples, n_features) Samples. C : array, shape [n_samples] Predicted class label per sample.
score(X, y, sample_weight=None)[source]

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters: X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. score : float Mean accuracy of self.predict(X) wrt. y.
set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns: self