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).

Input ports

1. Type: PortObject
   Model
   The trained model
2. Type: Table
   Predicted data
   The data with predicted classes and also the real class values

Output ports

1. Type: Table
   Boosting model
   The boosted models together with their weights in data table