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 builtin crossvalidation.
See glossary entry for crossvalidation estimator.
By default, it performs Generalized CrossValidation, which is a form of efficient LeaveOneOut crossvalidation. Currently, only the n_features > n_samples case is handled efficiently.
Read more in the User Guide.
 Parameters
 alphasndarray of shape (n_alphas,), default=(0.1, 1.0, 10.0)
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
1 / (2C)
in other linear models such asLogisticRegression
orsklearn.svm.LinearSVC
. fit_interceptbool, default=True
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
 normalizebool, 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 l2norm. If you wish to standardize, please usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
. scoringstring, callable, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. cvint, crossvalidation generator or an iterable, default=None
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the efficient LeaveOneOut crossvalidation
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
Refer User Guide for the various crossvalidation strategies that can be used here.
 class_weightdict or ‘balanced’, default=None
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_valuesbool, default=False
Flag indicating if the crossvalidation values corresponding to each alpha should be stored in the
cv_values_
attribute (see below). This flag is only compatible withcv=None
(i.e. using Generalized CrossValidation).
 Attributes
 cv_values_ndarray of shape (n_samples, n_targets, n_alphas), optional
Crossvalidation values for each alpha (if
store_cv_values=True
andcv=None
). Afterfit()
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). This attribute exists only whenstore_cv_values
is True. coef_ndarray of shape (1, n_features) or (n_targets, n_features)
Coefficient of the features in the decision function.
coef_
is of shape (1, n_features) when the given problem is binary. intercept_float or ndarray of shape (n_targets,)
Independent term in decision function. Set to 0.0 if
fit_intercept = False
. alpha_float
Estimated regularization parameter.
 best_score_float
Score of base estimator with best alpha.
 classes_ndarray of shape (n_classes,)
The classes labels.
See also
Ridge
Ridge regression
RidgeClassifier
Ridge classifier
RidgeCV
Ridge regression with builtin cross validation
Notes
For multiclass classification, n_class classifiers are trained in a oneversusall approach. Concretely, this is implemented by taking advantage of the multivariate response support in Ridge.
Examples
>>> from sklearn.datasets import load_breast_cancer >>> from sklearn.linear_model import RidgeClassifierCV >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = RidgeClassifierCV(alphas=[1e3, 1e2, 1e1, 1]).fit(X, y) >>> clf.score(X, y) 0.9630...
Methods
decision_function
(self, X)Predict confidence scores for samples.
fit
(self, X, y[, sample_weight])Fit Ridge classifier with cv.
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X)Predict class labels for samples in X.
score
(self, X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(self, \*\*params)Set the parameters of this estimator.

__init__
(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False)[source]¶ Initialize self. See help(type(self)) for accurate signature.

decision_function
(self, X)[source]¶ Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
 Parameters
 Xarray_like or sparse matrix, shape (n_samples, n_features)
Samples.
 Returns
 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
(self, X, y, sample_weight=None)[source]¶ Fit Ridge classifier with cv.
 Parameters
 Xndarray of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. When using GCV, will be cast to float64 if necessary.
 yndarray of shape (n_samples,)
Target values. Will be cast to X’s dtype if necessary.
 sample_weightfloat or ndarray of shape (n_samples,), default=None
Individual weights for each sample. If given a float, every sample will have the same weight.
 Returns
 selfobject

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)[source]¶ Predict class labels for samples in X.
 Parameters
 Xarray_like or sparse matrix, shape (n_samples, n_features)
Samples.
 Returns
 Carray, shape [n_samples]
Predicted class label per sample.

score
(self, X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.