Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression)
Globally replaces all missing values and transforms nominal attributes into binary ones.It also normalizes all attributes, so the coefficients in the output are based on the normalized data.
For numeric class attributes, the squared, Huber or epsilon-insensitve loss function must be used.Epsilon-insensitive and Huber loss may require a much higher learning rate.
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.