3.2.3.1.2. 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.
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 : 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 this 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 this 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 self.classes_[1] where >0 means this class would be predicted.
- 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.
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. This is parameter is deprecated.
Returns : self : object
Returns self.
- get_params(deep=True)¶
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)¶
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)
Test samples.
y : array-like, shape = (n_samples,)
True labels for X.
Returns : score : float
Mean accuracy of self.predict(X) wrt. y.
- set_params(**params)¶
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 :