| Issue | #Downvotes for this reason | By |
|---|
| C | Penalty parameter C of the error term | default: 0.001 |
| class_weight | default: null | |
| dual | Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features | default: false |
| 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 already centered) | default: true |
| intercept_scaling | When self.fit_intercept is True, instance vector x becomes ``[x, self.intercept_scaling]``, i.e. a "synthetic" feature with constant value equals 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 class_weight : {dict, 'balanced'}, optional Set the parameter C of class i to ``class_weight[i]*C`` for SVC. 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: 1 |
| loss | Specifies the loss function. 'hinge' is the standard SVM loss (used e.g. by the SVC class) while 'squared_hinge' is the square of the hinge loss | default: "squared_hinge" |
| max_iter | The maximum number of iterations to be run. | default: 1000 |
| multi_class | Determines the multi-class strategy if `y` contains more than two classes ``"ovr"`` trains n_classes one-vs-rest classifiers, while ``"crammer_singer"`` optimizes a joint objective over all classes While `crammer_singer` is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute If ``"crammer_singer"`` is chosen, the options loss, penalty and dual will be ignored | default: "ovr" |
| penalty | Specifies the norm used in the penalization. The 'l2' penalty is the standard used in SVC. The 'l1' leads to ``coef_`` vectors that are sparse | 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` | default: null |
| tol | Tolerance for stopping criteria | default: 0.001 |
| verbose | Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context | default: 0 |