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.
This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the User Guide.
Parameters: |
alpha : {float, array-like}, shape (n_targets) 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 copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. 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). max_iter : int, optional Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. This parameter is ignored when solver : {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’} Solver to use in the computational routines:
All last four solvers support both dense and sparse data. However, only ‘sag’ supports sparse input when New in version 0.17: Stochastic Average Gradient descent solver. tol : float Precision of the solution. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. Used only in ‘sag’ solver. New in version 0.17: random_state to support Stochastic Average Gradient. |
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Attributes: |
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 n_iter_ : array or None, shape (n_targets,) Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None. New in version 0.17. |
>>> 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)
decision_function (*args, **kwargs) | DEPRECATED: and will be removed in 0.19. |
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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decision_function(*args, **kwargs)
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DEPRECATED: and will be removed in 0.19.
Decision function of the linear model.
Parameters: |
X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples. |
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Returns: |
C : array, shape = (n_samples,) Returns predicted values. |
fit(X, y, sample_weight=None)
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Fit Ridge regression model
Parameters: |
X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training data y : array-like, shape = [n_samples] or [n_samples, n_targets] Target values sample_weight : float or numpy array of shape [n_samples] Individual weights for each sample |
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Returns: |
self : returns an instance of self. |
get_params(deep=True)
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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(X)
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Predict using the linear model
Parameters: |
X : {array-like, 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)
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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 regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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. 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_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.
Returns: | self : |
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sklearn.linear_model.Ridge
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html