| Issue | #Downvotes for this reason | By |
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| C | Inverse of regularization strength; must be a positive float Like in support vector machines, smaller values specify stronger regularization | 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))`` Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified .. versionadded:: 0.17 *class_weight='balanced'* instead of deprecated *class_weight='auto'* | default: null |
| dual | Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features | default: false |
| fit_intercept | Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function | default: true |
| intercept_scaling | Useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equal to intercept_scaling is appended to the instance vector The intercept becomes ``intercept_scaling * synthetic_feature_weight`` Note! the synthetic feature weight is subject to l1/l2 regularization as all other features To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased | default: 1 |
| max_iter | Useful only for the newton-cg, sag and lbfgs solvers Maximum number of iterations taken for the solvers to converge | default: 100 |
| multi_class | Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'newton-cg', 'sag' and 'lbfgs' solver .. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' case | default: "ovr" |
| n_jobs | Number of CPU cores used during the cross-validation loop. If given a value of -1, all cores are used. | default: 1 |
| penalty | Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties | default: "l2" |
| random_state | The seed of the pseudo random number generator to use when shuffling the data. Used only in solvers 'sag' and 'liblinear' solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag'}, default: 'liblinear' Algorithm to use in the optimization problem - For small datasets, 'liblinear' is a good choice, whereas 'sag' is faster for large ones - For multiclass problems, only 'newton-cg', 'sag' and 'lbfgs' handle multinomial loss; 'liblinear' is limited to one-versus-rest schemes - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty 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 .. versionadded:: 0.17 Stochastic Average Gradient descent solver | default: 13352 |
| solver | default: "liblinear" | |
| tol | Tolerance for stopping criteria | default: 0.0001 |
| verbose | For the liblinear and lbfgs solvers set verbose to any positive number for verbosity | default: 0 |
| warm_start | When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution Useless for liblinear solver .. versionadded:: 0.17 *warm_start* to support *lbfgs*, *newton-cg*, *sag* solvers | default: false |