| Issue | #Downvotes for this reason | By |
|---|
| alpha | default: 1.0 | |
| 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 (i.e. data is expected to be 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'}, default='auto' 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... | default: 0.001 |