sklearn.linear_model
.RidgeClassifierCV¶
- class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), *, fit_intercept=True, normalize='deprecated', scoring=None, cv=None, class_weight=None, store_cv_values=False)[source]¶
Ridge classifier with built-in cross-validation.
See glossary entry for cross-validation estimator.
By default, it performs Leave-One-Out Cross-Validation. 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
orLinearSVC
.- 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 l2-norm. If you wish to standardize, please useStandardScaler
before callingfit
on an estimator withnormalize=False
.Deprecated since version 1.0:
normalize
was deprecated in version 1.0 and will be removed in 1.2.- scoringstr, callable, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
.- cvint, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the efficient Leave-One-Out cross-validation
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
Refer User Guide for the various cross-validation 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 cross-validation 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 Leave-One-Out Cross-Validation).
- Attributes:
- cv_values_ndarray of shape (n_samples, n_targets, n_alphas), optional
Cross-validation values for each alpha (only if
store_cv_values=True
andcv=None
). Afterfit()
has been called, this attribute will contain the mean squared errors ifscoring is None
otherwise it will contain standardized per point prediction values.- 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.
New in version 0.23.
classes_
ndarray of shape (n_classes,)Classes labels.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
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.
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=[1e-3, 1e-2, 1e-1, 1]).fit(X, y) >>> clf.score(X, y) 0.9630...
Methods
Predict confidence scores for samples.
fit
(X, y[, sample_weight])Fit Ridge classifier with cv.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict class labels for samples in
X
.score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.
- property classes_¶
Classes labels.
- decision_function(X)[source]¶
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The data matrix for which we want to get the confidence scores.
- Returns:
- scoresndarray of shape (n_samples,) or (n_samples, n_classes)
Confidence scores per
(n_samples, n_classes)
combination. In the binary case, confidence score forself.classes_[1]
where >0 means this class would be predicted.
- fit(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 andn_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
Fitted estimator.
- get_params(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:
- paramsdict
Parameter names mapped to their values.
- predict(X)[source]¶
Predict class labels for samples in
X
.- Parameters:
- X{array-like, spare matrix} of shape (n_samples, n_features)
The data matrix for which we want to predict the targets.
- Returns:
- y_predndarray of shape (n_samples,) or (n_samples, n_outputs)
Vector or matrix containing the predictions. In binary and multiclass problems, this is a vector containing
n_samples
. In a multilabel problem, it returns a matrix of shape(n_samples, n_outputs)
.
- score(X, y, sample_weight=None)[source]¶
Return the mean accuracy on the given test data and labels.
In multi-label 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:
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
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
Pipeline
). 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:
- selfestimator instance
Estimator instance.