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.
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