3.2.4.1.9. sklearn.linear_model.RidgeCV — scikit-learn 0.20.4 documentation (original) (raw)
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.
See glossary entry for cross-validation estimator.
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 usesklearn.preprocessing.StandardScaler before calling fiton 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 signaturescorer(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. CV splitter, An iterable yielding (train, test) splits as arrays of indices. 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). |
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| 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 iffit_intercept = False. alpha_ : float Estimated regularization parameter. |
Examples
from sklearn.datasets import load_diabetes from sklearn.linear_model import RidgeCV X, y = load_diabetes(return_X_y=True) clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y) clf.score(X, y) 0.5166...
Methods
| fit(X, y[, sample_weight]) | Fit Ridge regression model |
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| 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 |
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| Returns: | self : object |
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. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
Predict using the linear model
| Parameters: | X : array_like or sparse matrix, shape (n_samples, n_features) Samples. |
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| 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. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. 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. |
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| Returns: | score : float R^2 of self.predict(X) wrt. y. |
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 |
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