8.17.24. sklearn.linear_model.RidgeClassifierCV

class sklearn.linear_model.RidgeClassifierCV(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None, class_weight=None)

Ridge classifier with built-in cross-validation.

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.

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 (2*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.

score_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediction (big is good) if None is passed, the score of the estimator is maximized

loss_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediction (small is good) if None is passed, the score of the estimator is maximized

cv : cross-validation generator, optional

If None, Generalized Cross-Validation (efficient Leave-One-Out) will be used.

class_weight : dict, optional

Weights associated with classes in the form {class_label : weight}. If not given, all classes are supposed to have weight one.

See also

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.

Attributes

cv_values_	array, shape = [n_samples, n_alphas] or shape = [n_samples, n_responses, 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).
alpha_	float	Estimated regularization parameter

Methods

decision_function(X)	Predict confidence scores for samples.
fit(X, y[, sample_weight, class_weight])	Fit the ridge classifier.
get_params([deep])	Get parameters for the estimator
predict(X)	Predict class labels for samples in X.
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.

__init__(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None, class_weight=None)

decision_function(X)

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, 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 the “positive” class.

fit(X, y, sample_weight=1.0, class_weight=None)

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.

sample_weight : float or numpy array of shape [n_samples]

Sample weight

Returns:

self : object

Returns self.

get_params(deep=True)

Get parameters for the estimator

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

Predict class labels for samples in X.

Parameters:

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Samples.

Returns:

C : array, shape = [n_samples]

Predicted class label per sample.

score(X, y)

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

Parameters:

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns:

z : float

set_params(**params)

Set the parameters of the 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 :