9.2.3. 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)¶
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).
normalize : boolean, optional
If True, the regressors X are normalized
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
Methods
fit predict score set_params - __init__(alphas=array([ 0.1, 1., 10. ]), fit_intercept=True, normalize=False, score_func=None, loss_func=None, cv=None)¶
- 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
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
- 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 :