Boosting Learner Loop End

LoopEnd

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

Extension

This node is part of the extension

KNIME Ensemble Learning Wrappers

v4.0.0

Short Link

Drag node into KNIME Analytics Platform