| Issue | #Downvotes for this reason | By |
|---|
| alphas | Array of alpha values to try 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: [0.1, 1.0, 10.0] |
| 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))`` | default: null |
| cv | Determines the cross-validation splitting strategy
Possible inputs for cv are:
- None, to use the efficient Leave-One-Out cross-validation
- integer, to specify the number of folds
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices
Refer :ref:`User Guide | default: null |
| 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 |
| scoring | A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)`` | default: null |
| store_cv_values | Flag indicating if the cross-validation values corresponding to each alpha should be stored in the ``cv_values_`` attribute (see below). This flag is only compatible with ``cv=None`` (i.e. using Leave-One-Out Cross-Validation). | default: false |