Identifying variables in Boosting Trees


I would like to know what variables are finally used in the determination of a boosting process in decision tree analysis:

boosting learner loop star-decision tree learner-decision tree predictor-boosting learner loop end.

Is the result transparent?

Thank you very much.



Hi Svalerov,

I'm not sure if I understand your question. The output of the boosting is a table of weighted models  (also called weak classifiers) which are then combined to a global model (ensemble). The weights of the models are determined using a variant of AdaBoost, called AdaBoost.SAMME which reweights the instances during training in each iteration. Instances which are classified correctly by the ensemble in the current iteration have a lower weight, instances which are classified obtain a higher weight. Like that instances which are harder to classify will have more influence on the selection of the next weak classifeir.

Does this help?




This may help, too (taken from

16:45 – 17:30 Special Session:
  • Dean Abbott (Abbott Analytics)Measuring Variable Importance with Target Shuffling

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Works for any model!