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)[source]¶ Ridge classifier with builtin crossvalidation.
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: alphas : numpy array of shape [n_alphas]
Array of alpha values to try. Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. 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
If True, the regressors X will be normalized before regression.
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, crossvalidation generator or an iterable, optional
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 object to be used as a crossvalidation generator.
 An iterable yielding train/test splits.
Refer User Guide for the various crossvalidation 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))
Attributes: cv_values_ : array, shape = [n_samples, n_alphas] or shape = [n_samples, n_responses, n_alphas], optional
Crossvalidation 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
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.
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)[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 : {arraylike, 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
(X, y, sample_weight=None)[source]¶ Fit the ridge classifier.
Parameters: X : arraylike, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
y : arraylike, shape (n_samples,)
Target values.
sample_weight : float or numpy array of shape (n_samples,)
Sample weight.
Returns: self : object
Returns self.

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.
Returns: params : mapping of string to any
Parameter names mapped to their values.

predict
(X)[source]¶ Predict class labels for samples in X.
Parameters: X : {arraylike, sparse matrix}, shape = [n_samples, n_features]
Samples.
Returns: 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 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: X : arraylike, shape = (n_samples, n_features)
Test samples.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns: 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 former have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: self :