8.17.23. 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')

Classifier using Ridge regression.

Parameters:

alpha : float

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.

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.

copy_X : boolean, optional, default True

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

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).

max_iter : int, optional

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

normalize : boolean, optional, default False

If True, the regressors X will be normalized before regression.

solver : {‘auto’, ‘dense_cholesky’, ‘lsqr’, ‘sparse_cg’}

Solver to use in the computational routines. ‘dense_cholesky’ will use the standard scipy.linalg.solve function, ‘sparse_cg’ will use the conjugate gradient solver as found in scipy.sparse.linalg.cg while ‘auto’ will chose the most appropriate depending on the matrix X. ‘lsqr’ uses a direct regularized least-squares routine provided by scipy.

tol : float

Precision of the solution.

See also

Ridge, RidgeClassifierCV

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

coef_

array, shape = [n_features] or [n_classes, n_features]

Weight vector(s).

Methods

decision_function(X)	Predict confidence scores for samples.
fit(X, y[, solver])	Fit Ridge regression model.
get_params([deep])	Get parameters for the 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 the estimator.

__init__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, class_weight=None, solver='auto')

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 the “positive” class.

fit(X, y, solver=None)

Fit Ridge regression model.

Parameters:

X : {array-like, sparse matrix}, shape = [n_samples,n_features]

Training data

y : array-like, shape = [n_samples]

Target values

Returns:

self : returns an instance of self.

get_params(deep=True)

Get parameters for the estimator

Parameters:

deep: boolean, optional :

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

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]

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns:

z : float

set_params(**params)

Set the parameters of the 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 :