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sklearn.linear_model.ridge.Ridge

Visibility: public Uploaded 18-12-2019 by George Volkov
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9 5 runs

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alpha | default: 0.17 | |

copy_X | If True, X will be copied; else, it may be overwritten | default: true |

fit_intercept | 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) | default: true |

max_iter | 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 | default: null |

normalize | 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 :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False`` | default: false |

random_state | The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag' .. versionadded:: 0.17 *random_state* to support Stochastic Average Gradient. | default: null |

solver | default: "auto" | |

tol | Precision of the solution solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'} 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 closed-form 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 large-scale data (possibility to set `tol` and `max_iter`) - 'lsqr' uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses its improved, unbiased version named SAGA. Both methods also use ... | default: 0.001 |

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