8.15.1.3. sklearn.linear_model.RidgeClassifier

class sklearn.linear_model.RidgeClassifier(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, tol=0.001)

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.

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

If True, the regressors X are normalized

copy_X : boolean, optional, default True

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

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)
fit(X, y[, solver])	Fit Ridge regression model.
get_params([deep])	Get parameters for the estimator
predict(X)	Predict target values according to the fitted model.
score(X, y)	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of the estimator.

__init__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, tol=0.001)

fit(X, y, solver='auto')

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

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

Solver to use in the computational routines. ‘delse_cholesky’ will use the standard scipy.linalg.solve function, ‘sparse_cg’ will use the a conjugate gradient solver as found in scipy.sparse.linalg.cg while ‘auto’ will chose the most appropiate depending on the matrix X.

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 target values according to the fitted model.

Parameters :X : array-like, shape = [n_samples, n_features] Returns :y : array, shape = [n_samples]

score(X, y)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

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 :