class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver=’auto’, random_state=None)
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Linear least squares with l2 regularization.
Minimizes the objective function:
y  Xw^2_2 + alpha * w^2_2
This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has builtin support for multivariate regression (i.e., when y is a 2darray of shape [n_samples, n_targets]).
Read more in the User Guide.
Parameters: 


Attributes: 

See also
RidgeClassifier
RidgeCV
sklearn.kernel_ridge.KernelRidge
>>> from sklearn.linear_model import Ridge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = Ridge(alpha=1.0) >>> clf.fit(X, y) Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001)
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__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver=’auto’, random_state=None)
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fit(X, y, sample_weight=None)
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Fit Ridge regression model
Parameters: 


Returns: 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
[source]
Predict using the linear model
Parameters: 


Returns: 

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: 


Returns: 

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: 


sklearn.linear_model.Ridge
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.linear_model.Ridge.html