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.
- Type: Data Any input data with nominal class labels
- Type: Data Possibly re-sampled training data, must be connected to the learner node inside the loop
- Type: Data Unaltered input data, must be connected to the predictor node inside the loop
Analytics > Mining > Ensemble Learning
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