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Boosting Learner Loop Start

KNIME Ensemble Learning version 4.3.0.v202011191423 by KNIME AG, Zurich, Switzerland

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

Best Friends (Incoming)

Best Friends (Outgoing)



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A zipped version of the software site can be downloaded here.

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