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)
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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. 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 l2norm. 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, 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)) 
store_cv_values : boolean, 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 with cv=None (i.e. using Generalized CrossValidation). 
Attributes: 

cv_values_ : array, shape = [n_samples, n_targets, 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 
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 (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)
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decision_function(X)
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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. 
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)
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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. Will be cast to X’s dtype if necessary 
sample_weight : float or numpy array of shape (n_samples,) 
Sample weight. 
Returns: 

self : object 

get_params(deep=True)
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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)
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Predict class labels for samples in X.
Parameters: 

X : array_like or 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)
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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)
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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.