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NodeNode / LoopEnd

Boosting Learner Loop End

Analytics Mining Ensemble Learning
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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).

Node details

Input ports
  1. Type: PortObject
    Model
    The trained model
  2. Type: Table
    Predicted data
    The data with predicted classes and also the real class values
Output ports
  1. Type: Table
    Boosting model
    The boosted models together with their weights in data table

Extension

The Boosting Learner Loop End node is part of this extension:

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