3.2.4.1.9. sklearn.linear_model.RidgeCV

class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False) [source]

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.

Read more in the User Guide.

Parameters:

alphas : numpy array of shape [n_alphas] Array of alpha values to try. 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 l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False. 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 : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the efficient Leave-One-Out cross-validation integer, to specify the number of folds. An object to be used as a cross-validation generator. An iterable yielding train/test splits. For integer/None inputs, if y is binary or multiclass, sklearn.model_selection.StratifiedKFold is used, else, sklearn.model_selection.KFold is used. Refer User Guide for the various cross-validation strategies that can be used here. 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).

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). intercept_ : float | array, shape = (n_targets,) Independent term in decision function. Set to 0.0 if fit_intercept = False. alpha_ : float Estimated regularization parameter.

See also

Ridge

Ridge regression

RidgeClassifier

Ridge classifier

RidgeClassifierCV

Ridge classifier with built-in cross validation

Methods

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[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False) [source]

fit(X, y, sample_weight=None) [source]

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. Will be cast to X’s dtype if necessary sample_weight : float or array-like of shape [n_samples] Sample weight
Returns:	self : Returns self.

get_params(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(X) [source]

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, sample_weight=None) [source]

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

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float R^2 of self.predict(X) wrt. y.

set_params(**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 :

3.2.4.1.9.1. Examples using sklearn.linear_model.RidgeCV