Meta classifier that enhances the performance of a regression base classifier

Each iteration fits a model to the residuals left by the classifier on the previous iteration.Prediction is accomplished by adding the predictions of each classifier.

Reducing the shrinkage (learning rate) parameter helps prevent overfitting and has a smoothing effect but increases the learning time.

For more information see:

J.H. Friedman (1999). Stochastic Gradient Boosting.

(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.