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

Output ports

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