3.2.4.1.9. sklearn.linear_model.RidgeCV
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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)
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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: |
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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: |
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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. |
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 |
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. |
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__init__(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None, store_cv_values=False)
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fit(X, y, sample_weight=None)
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Fit Ridge regression model
Parameters: |
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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: |
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self : object |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
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deep : boolean, optional -
If True, will return the parameters for this estimator and contained subobjects that are estimators. |
Returns: |
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params : mapping of string to any -
Parameter names mapped to their values. |
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predict(X)
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Predict using the linear model
Parameters: |
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X : array_like or sparse matrix, shape (n_samples, n_features) -
Samples. |
Returns: |
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C : array, shape (n_samples,) -
Returns predicted values. |
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score(X, y, sample_weight=None)
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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: |
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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. |
Returns: |
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score : float -
R^2 of self.predict(X) wrt. y. |
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set_params(**params)
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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.
3.2.4.1.9.1. Examples using sklearn.linear_model.RidgeCV