| Issue | #Downvotes for this reason | By |
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| alpha | 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 ``1 / (2C)`` in other linear models such as :class:`~sklearn.linear_model.LogisticRegression` or :class:`~sklearn.svm.LinearSVC` | default: 1.0 |
| class_weight | Weights associated with classes in the form ``{class_label: weight}`` If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga', 'lbfgs'}, 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. It is the most stable solver, in particular more stable for singular matrices than 'cholesky' at the cost of being slower - '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... | default: null |
| 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 The default value is determined by scipy.sparse.linalg | default: null |
| positive | When set to ``True``, forces the coefficients to be positive Only 'lbfgs' solver is supported in this case | default: false |
| random_state | Used when ``solver`` == 'sag' or 'saga' to shuffle the data
See :term:`Glossary | default: null |
| solver | default: "auto" | |
| tol | Precision of the solution. Note that `tol` has no effect for solvers 'svd' and 'cholesky' .. versionchanged:: 1.2 Default value changed from 1e-3 to 1e-4 for consistency with other linear models | default: 0.0001 |