8.15.1.4. sklearn.linear_model.RidgeCV

class sklearn.linear_model.RidgeCV(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None, gcv_mode=None)

Ridge regression with built-in cross-validation.

By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation.

Parameters :

alphas: numpy array of shape [n_alpha] :

Array of alpha values to try. 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

score_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediction (big is good) if None is passed, the score of the estimator is maximized

loss_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediction (small is good) if None is passed, the score of the estimator is maximized

cv : cross-validation generator, optional

If None, Generalized Cross-Validation (efficient Leave-One-Out) will be used.

gcv_mode: {None, ‘auto’, ‘svd’, eigen’}, optional :

Flag indicating which strategy to use when performing Generalized Cross-Validation. Options are:

'auto' : use svd if n_samples > n_features, otherwise use eigen
'svd' : force computation via svd of X
'eigen' : force computation via eigendecomposition of X^T X

The ‘auto’ mode is the default and is intended to pick the cheaper option of the two depending upon the shape of the training data.

See also

Ridge, RidgeClassifierCV

Methods

decision_function(X)	Decision function of the linear model
fit(X, y[, sample_weight])	Fit Ridge regression model
get_params([deep])	Get parameters for the estimator
predict(X)	Predict using the linear model
score(X, y)	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of the estimator.

__init__(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None, gcv_mode=None)

decision_function(X)

Decision function of the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

fit(X, y, sample_weight=1.0)

Fit Ridge regression model

Parameters :

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

Training data

y : array-like, shape = [n_samples] or [n_samples, n_responses]

Target values

sample_weight : float or array-like of shape [n_samples]

Sample weight

Returns :

self : Returns 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 using the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

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 :