sklearn.linear_model
.RidgeClassifier¶

class
sklearn.linear_model.
RidgeClassifier
(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, class_weight=None, solver=’auto’, random_state=None)[source]¶ Classifier using Ridge regression.
Read more in the User Guide.
Parameters:  alpha : float
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 usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
. copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
 max_iter : int, optional
Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
 tol : float
Precision of the solution.
 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))
 solver : {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’}
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. More stable for singular matrices than ‘cholesky’.
‘cholesky’ uses the standard scipy.linalg.solve function to obtain a closedform solution.
‘sparse_cg’ uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for largescale data (possibility to set
tol
and max_iter).‘lsqr’ uses the dedicated regularized leastsquares 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.
 random_state : int, RandomState instance or None, optional, default None
The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by
np.random
. Used whensolver
== ‘sag’.
Attributes:  coef_ : array, 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  array, shape = (n_targets,)
Independent term in decision function. Set to 0.0 if
fit_intercept = False
. n_iter_ : array or None, shape (n_targets,)
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
See also
Ridge
 Ridge regression
RidgeClassifierCV
 Ridge classifier with builtin cross validation
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 RidgeClassifier >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = RidgeClassifier().fit(X, y) >>> clf.score(X, y) 0.9595...
Methods
decision_function
(self, X)Predict confidence scores for samples. fit
(self, X, y[, sample_weight])Fit Ridge regression model. get_params
(self[, deep])Get parameters for this estimator. predict
(self, X)Predict class labels for samples in X. score
(self, X, y[, sample_weight])Returns the mean accuracy on the given test data and labels. set_params
(self, \*\*params)Set the parameters of this estimator. 
__init__
(self, alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, class_weight=None, solver=’auto’, random_state=None)[source]¶

decision_function
(self, X)[source]¶ 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
(self, X, y, sample_weight=None)[source]¶ Fit Ridge regression model.
Parameters:  X : {arraylike, sparse matrix}, shape = [n_samples,n_features]
Training data
 y : arraylike, shape = [n_samples]
Target values
 sample_weight : float or numpy array of shape (n_samples,)
Sample weight.
New in version 0.17: sample_weight support to Classifier.
Returns:  self : returns an instance of self.

get_params
(self, deep=True)[source]¶ 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
(self, X)[source]¶ 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
(self, X, y, sample_weight=None)[source]¶ 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
(self, **params)[source]¶ 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.Returns:  self