Boosting Learner Loop End
Together with the corresponding loop start node a boosting loop can be constructed. It repeatedly trains simple models and weighs them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The loop is stopped either after the maximum number of iterations has been reached or the weight for a model is only slightly above 0 (meaning the prediction error is too big).
- Type: PortObject The trained model
- Type: Data The data with predicted classes and also the real class values
- Type: Data The boosted models together with their weights in data table