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


This node is part of the extension

KNIME Ensemble Learning Wrappers


Short Link

Drag node into KNIME Analytics Platform