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 The trained model
  2. Type: Data The data with predicted classes and also the real class values

Output Ports

  1. Type: Data The boosted models together with their weights in data table

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Analytics > Mining > Ensemble Learning

Make sure to have this extension installed:

KNIME Ensemble Learning Wrappers

Update site for KNIME Analytics Platform 3.7:
KNIME Analytics Platform 3.7 Update Site

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