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Linear least squares with l2 regularization. This node has been automatically generated by wrapping the ``sklearn.linear_model.ridge.Ridge`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. 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 :ref:`User Guide <ridge_regression>`. **Parameters** alpha : {float, arraylike}, shape (n_targets) Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to ``C^1`` in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. 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. solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag'} Solver to use in the computational routines:  'auto' chooses the solver automatically based on the type of data.  'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than 'cholesky'.  'cholesky' uses the standard scipy.linalg.solve function to obtain a closedform solution.  'sparse_cg' uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than 'cholesky' for largescale data (possibility to set `tol` and `max_iter`).  'lsqr' uses the dedicated regularized leastsquares routine scipy.sparse.linalg.lsqr. It is the fatest but may not be available in old scipy versions. It also uses an iterative procedure.  'sag' uses a Stochastic Average Gradient descent. It also uses an iterative procedure, and is often faster than other solvers when both n_samples and n_features are large. Note that 'sag' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. All last four solvers support both dense and sparse data. However, only 'sag' supports sparse input when `fit_intercept` is True. .. versionadded:: 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 in 'sag' solver. .. versionadded:: 0.17 *random_state* to support Stochastic Average Gradient. **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 ``fit_intercept = False``. ``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. See also RidgeClassifier, RidgeCV, KernelRidge **Examples** >>> 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) # doctest: +NORMALIZE_WHITESPACE Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001)














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supported_dtypes Supported dtypes 

Linear least squares with l2 regularization. This node has been automatically generated by wrapping the ``sklearn.linear_model.ridge.Ridge`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. 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 :ref:`User Guide <ridge_regression>`. **Parameters** alpha : {float, arraylike}, shape (n_targets) Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to ``C^1`` in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. 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. solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag'} Solver to use in the computational routines:  'auto' chooses the solver automatically based on the type of data.  'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than 'cholesky'.  'cholesky' uses the standard scipy.linalg.solve function to obtain a closedform solution.  'sparse_cg' uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than 'cholesky' for largescale data (possibility to set `tol` and `max_iter`).  'lsqr' uses the dedicated regularized leastsquares routine scipy.sparse.linalg.lsqr. It is the fatest but may not be available in old scipy versions. It also uses an iterative procedure.  'sag' uses a Stochastic Average Gradient descent. It also uses an iterative procedure, and is often faster than other solvers when both n_samples and n_features are large. Note that 'sag' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. All last four solvers support both dense and sparse data. However, only 'sag' supports sparse input when `fit_intercept` is True. .. versionadded:: 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 in 'sag' solver. .. versionadded:: 0.17 *random_state* to support Stochastic Average Gradient. **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 ``fit_intercept = False``. ``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. See also RidgeClassifier, RidgeCV, KernelRidge **Examples** >>> 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) # doctest: +NORMALIZE_WHITESPACE Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver='auto', tol=0.001)




Predict using the linear model This node has been automatically generated by wrapping the sklearn.linear_model.ridge.Ridge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns



Fit Ridge regression model This node has been automatically generated by wrapping the sklearn.linear_model.ridge.Ridge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns self : returns an instance of self.

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