scikits.learn.linear_model.RidgeCV

class scikits.learn.linear_model.RidgeCV(alphas=array([, 0.1, 1., 10.], ), fit_intercept=True, score_func=None, loss_func=None, cv=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. Currently, only the n_features > n_samples case is handled efficiently.

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).

loss_func: callable, optional :

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

score_func: callable, optional :

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

See also

Ridge

Methods

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

fit(X, y, sample_weight=1.0, **params)

Fit Ridge regression model

Parameters :

X : numpy array of shape [n_samples, n_features]

Training data

y : numpy array of shape [n_samples] or [n_samples, n_responses]

Target values

sample_weight : float or numpy array of shape [n_samples]

Sample weight

cv : cross-validation generator, optional

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

Returns :

self : Returns self.

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 of the prediction

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float