Boosting Learner Loop Start


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 part of the extension

KNIME Ensemble Learning Wrappers


Short Link

Drag node into KNIME Analytics Platform