LinearSVC MultiLinearSVC classification

Golearn includes liblinear, used for logistic regression and also linear support vector classification. It's best suited to datasets containing only a large number of numeric attributes, which often occur in natural language processing.

LinearSVC

• The LinearSVC classifier outputs a binary class value (either 0 or 1).
• Only FloatAttributes are used as input.
• Only one class Attribute is supported.
• Training requires conversion of the dataset, which may cause memory presure (see issue #94).
• Prediction requires conversion of only the current row.
• Penalty and loss parameters can be either "l1" or "l2". Not all combinations are supported.
• The dual parameter decides whether liblinear optimises the primal or dual form, some choices are incompatible with combinations of "l1" and "l2".
• C is roughly the "penalty" parameter.
• eps decides when to stop iterating. Smaller values typically take longer.

MultiLinearSVC

The MultiLinearSVC can output a categorical class value, and uses the OneVsAllModel meta-classifier to output any CategoricalAttribute value. It works by training n binary LinearSVC classifiers - one for each given class - and classifying a instance as a given class when one of the underlying LinearSVC classifiers reports 1. Parameters and other things are precisely the same as the LinearSVC.

Limitatations

• Currently, per-class weights are unsupported.
• Only FloatAttributes are currently supported.