3.2.3.1.1. sklearn.linear_model.RidgeCV¶

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

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_alphas] :

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, default False

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

scoring : string, callable or None, optional, default: None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

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 or when X is a sparse
         matrix, otherwise use eigen
'svd' : force computation via singular value decomposition of X
        (does not work for sparse matrices)
'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 and format of the training data.

store_cv_values : boolean, default=False

Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv_values_ attribute (see below). This flag is only compatible with cv=None (i.e. using Generalized Cross-Validation).

See also

Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeClassifierCV: Ridge classifier with built-in cross validation

Attributes

cv_values_	array, shape = [n_samples, n_alphas] or shape = [n_samples, n_targets, n_alphas], optional	Cross-validation values for each alpha (if store_cv_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors (by default) or the values of the {loss,score}_func function (if provided in the constructor).
coef_	array, shape = [n_features] or [n_targets, n_features]	Weight vector(s).
alpha_	float	Estimated regularization parameter.
intercept_	float \| array, shape = (n_targets,)	Independent term in decision function. Set to 0.0 if `fit_intercept = False`.

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 this 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 this estimator.

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

decision_function(X)¶

Decision function of the linear model.

Parameters :

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

Samples.

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_targets]

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 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(X)¶

Predict using the linear model

Parameters :

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

Samples.

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_true - 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)

Test samples.

y : array-like, shape = (n_samples,)

True values for X.

Returns :

score : float

R^2 of self.predict(X) wrt. y.

set_params(**params)¶

Set the parameters of this 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 :