Boosting Learner Loop Start

Together with the corresponding loop end node a boosting loop can be constructed. It repeatedly trains simple models and weights them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The first output contains the re- and over-sampled dataset, rows that have been predicted wrong are contained more often than correctly predicted rows.

Input Ports

Any input data with nominal class labels

Output Ports

Possibly re-sampled training data, must be connected to the learner node inside the loop
Unaltered input data, must be connected to the predictor node inside the loop


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