| Issue | #Downvotes for this reason | By |
|---|
| C | Inverse of regularization strength; must be a positive float Like in support vector machines, smaller values specify stronger regularization | default: 0.001 |
| 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'* | default: "balanced" |
| 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: 10000 |
| multi_class | If the option chosen is 'ovr', then a binary problem is fit for each label. For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, *even when the data is binary*. 'multinomial' is unavailable when solver='liblinear' 'auto' selects 'ovr' if the data is binary, or if solver='liblinear', and otherwise selects 'multinomial' .. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' case .. versionchanged:: 0.20 Default will change from 'ovr' to 'auto' in 0.22 | default: "auto" |
| n_jobs | Number of CPU cores used when parallelizing over classes if
multi_class='ovr'". This parameter is ignored when the ``solver`` is
set to 'liblinear' regardless of whether 'multi_class' is specified or
not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors
See :term:`Glossary | default: null |
| penalty | Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties .. versionadded:: 0.19 l1 penalty with SAGA solver (allowing 'multinomial' + L1) | default: "l2" |
| 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' or 'liblinear' | default: null |
| solver | Algorithm to use in the optimization problem - For small datasets, 'liblinear' is a good choice, whereas 'sag' and 'saga' are faster for large ones - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs' handle multinomial loss; 'liblinear' is limited to one-versus-rest schemes - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty, whereas 'liblinear' and 'saga' handle L1 penalty Note that 'sag' and 'saga' 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 .. versionadded:: 0.19 SAGA solver .. versionchanged:: 0.20 Default will change from 'liblinear' to 'lbfgs' in 0.22 | default: "lbfgs" |
| 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. See :term:`the Glossary | default: false |