class sklearn.linear_model.RidgeClassifier(alpha=1.0, *, fit_intercept=True, copy_X=True, max_iter=None, tol=0.0001, class_weight=None, solver='auto', positive=False, random_state=None)[source]

Classifier using Ridge regression.

This classifier first converts the target values into {-1, 1} and then treats the problem as a regression task (multi-output regression in the multiclass case).

Read more in the User Guide.

alphafloat, default=1.0

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 as LogisticRegression or LinearSVC.

fit_interceptbool, default=True

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).

copy_Xbool, default=True

If True, X will be copied; else, it may be overwritten.

max_iterint, default=None

Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.

tolfloat, default=1e-4

Precision of the solution. Note that tol has no effect for solvers ‘svd’ and ‘cholesky’.

Changed in version 1.2: Default value changed from 1e-3 to 1e-4 for consistency with other linear models.

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)).

solver{‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’, ‘lbfgs’}, default=’auto’

Solver to use in the computational routines:

  • ‘auto’ chooses the solver automatically based on the type of data.

  • ‘svd’ uses a Singular Value Decomposition of X to compute the Ridge coefficients. It is the most stable solver, in particular more stable for singular matrices than ‘cholesky’ at the cost of being slower.

  • ‘cholesky’ uses the standard scipy.linalg.solve function to obtain a closed-form solution.

  • ‘sparse_cg’ uses the conjugate gradient solver as found in As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for large-scale data (possibility to set tol and max_iter).

  • ‘lsqr’ uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure.

  • ‘sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses its unbiased and more flexible version named SAGA. Both methods use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.

    New in version 0.17: Stochastic Average Gradient descent solver.

    New in version 0.19: SAGA solver.

  • ‘lbfgs’ uses L-BFGS-B algorithm implemented in scipy.optimize.minimize. It can be used only when positive is True.

positivebool, default=False

When set to True, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case.

random_stateint, RandomState instance, default=None

Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.

coef_ndarray of shape (1, n_features) or (n_classes, 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.

n_iter_None or ndarray of shape (n_targets,)

Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.

classes_ndarray of shape (n_classes,)

Classes labels.


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 regression.


Ridge classifier with built-in cross validation.


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.


>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import RidgeClassifier
>>> X, y = load_breast_cancer(return_X_y=True)
>>> clf = RidgeClassifier().fit(X, y)
>>> clf.score(X, y)



Predict confidence scores for samples.

fit(X, y[, sample_weight])

Fit Ridge classifier model.


Get parameters for this estimator.


Predict class labels for samples in X.

score(X, y[, sample_weight])

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


Set the parameters of this estimator.

property classes_

Classes labels.


Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

X{array-like, sparse matrix} of shape (n_samples, n_features)

The data matrix for which we want to get the confidence scores.

scoresndarray of shape (n_samples,) or (n_samples, n_classes)

Confidence scores per (n_samples, n_classes) 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)[source]

Fit Ridge classifier model.

X{ndarray, sparse matrix} of shape (n_samples, n_features)

Training data.

yndarray of shape (n_samples,)

Target values.

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.

New in version 0.17: sample_weight support to RidgeClassifier.


Instance of the estimator.


Get parameters for this estimator.

deepbool, default=True

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


Parameter names mapped to their values.


Predict class labels for samples in X.

X{array-like, spare matrix} of shape (n_samples, n_features)

The data matrix for which we want to predict the targets.

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.

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.


Mean accuracy of self.predict(X) w.r.t. y.


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.


Estimator parameters.

selfestimator instance

Estimator instance.

Examples using sklearn.linear_model.RidgeClassifier

Classification of text documents using sparse features

Classification of text documents using sparse features

Classification of text documents using sparse features