NodeNode / LoopStart

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. Input Type: Data
    Any input data with nominal class labels
Output ports
  1. Training data Type: Data
    Possibly re-sampled training data, must be connected to the learner node inside the loop
  2. Output Type: Data
    Unaltered input data, must be connected to the predictor node inside the loop
  1. Random Forest, Gradient Boosted Trees, and TreeEnsemble
    classification machine learning prediction +9
    This workflow solves a binary classification problem on the adult dataset using more advanced algorithms: - Random Fore…
    rs > Public > Tree_Ensembles