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Boosting Learner Loop Start

AnalyticsMiningEnsemble Learning
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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.

Node details

Input ports
  1. Type: Table
    Input
    Any input data with nominal class labels
Output ports
  1. Type: Table
    Training data
    Possibly re-sampled training data, must be connected to the learner node inside the loop
  2. Type: Table
    Output
    Unaltered input data, must be connected to the predictor node inside the loop

Extension

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

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Related workflows & nodes

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