RidgeClassifierCV#
- class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), *, fit_intercept=True, scoring=None, cv=None, class_weight=None, store_cv_results=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:
- alphasarray-like 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
. If using Leave-One-Out cross-validation, alphas must be strictly positive.- 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).
- scoringstr, callable, default=None
The scoring method to use for cross-validation. Options:
str: see String name scorers for options.
callable: a scorer callable object (e.g., function) with signature
scorer(estimator, X, y)
. See Callable scorers for details.None
: negative mean squared error if cv is None (i.e. when using leave-one-out cross-validation), or accuracy otherwise.
- 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_resultsbool, default=False
Flag indicating if the cross-validation results corresponding to each alpha should be stored in the
cv_results_
attribute (see below). This flag is only compatible withcv=None
(i.e. using Leave-One-Out Cross-Validation).Changed in version 1.5: Parameter name changed from
store_cv_values
tostore_cv_results
.
- Attributes:
- cv_results_ndarray of shape (n_samples, n_targets, n_alphas), optional
Cross-validation results for each alpha (only if
store_cv_results=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.Changed in version 1.5:
cv_values_
changed tocv_results_
.- 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.
Added in version 0.23.
classes_
ndarray of shape (n_classes,)Classes labels.
- n_features_in_int
Number of features seen during fit.
Added 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.Added 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...
- 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, **params)[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.
- **paramsdict, default=None
Parameters to be passed to the underlying scorer.
Added in version 1.5: Only available if
enable_metadata_routing=True
, which can be set by usingsklearn.set_config(enable_metadata_routing=True)
. See Metadata Routing User Guide for more details.
- Returns:
- selfobject
Fitted estimator.
- get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.5.
- Returns:
- routingMetadataRouter
A
MetadataRouter
encapsulating routing information.
- 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 accuracy on provided 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)
w.r.t.y
.
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') RidgeClassifierCV [source]#
Configure whether metadata should be requested to be passed to the
fit
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- 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.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') RidgeClassifierCV [source]#
Configure whether metadata should be requested to be passed to the
score
method.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True
(seesklearn.set_config
). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.