Boosting Learner Loop Start

LoopStart

Together with the corresponding loop end node a boosting loop can be constructed. It repeatedly trains simple models and weights them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The first output contains the re- and over-sampled dataset, rows that have been predicted wrong are contained more often than correctly predicted rows.

Input Ports

  1. Type: Data Any input data with nominal class labels

Output Ports

  1. Type: Data Possibly re-sampled training data, must be connected to the learner node inside the loop
  2. Type: Data Unaltered input data, must be connected to the predictor node inside the loop
This node is not known to be contained in any workflow.

Find here

Analytics > Mining > Ensemble Learning

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