Boosting Learner Loop End

Together with the corresponding loop start node a boosting loop can be constructed. It repeatedly trains simple models and weighs them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The loop is stopped either after the maximum number of iterations has been reached or the weight for a model is only slightly above 0 (meaning the prediction error is too big).


Real class column
The column from the second input table that contains the real class values for each row
Predicted class column
The column from the second input table that contains the predicted class values for each row
Number of iterations
The number of iterations the loop should be run i.e. the number of models to be learned
Use seed for random numbers
Check this option if you want to use a fixed see for generating random numbers. This ensures that for the same input data always the same sets in each iteration are created.
Enter the seed for the random number generator here.

Input Ports

The trained model
The data with predicted classes and also the real class values

Output Ports

The boosted models together with their weights in data table


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