document: Predicting rare classes : can boosting make any weak learner strong ? Boosting is a strong ensemble-based learning algorithm with the promise of iteratively improving the classification accuracy using any base learner , as long as it satisfies the condition of yielding weighted accuracy ) 0.5 . In this paper , we analyze boosting with respect to this basic condition on the base learner , to see if boosting ensures prediction of rarely occurring events with high recall and precision . First we show that a base learner can satisfy the required condition even for poor recall or precision levels , especially for very rare classes . Furthermore , we show that the intelligent weight updating mechanism in boosting , even in its strong cost-sensitive form , does not prevent cases where the base learner always achieves high precision but poor recall or high recall but poor precision , when mapped to the original distribution . In either of these cases , we show that the voting mechanism of boosting falls to achieve good overall recall and precision for the ensemble . In effect , our analysis indicates that one can not be blind to the base learner performance , and just rely on the boosting mechanism to take care of its weakness . We validate our arguments empirically on variety of real and synthetic rare class problems . In particular , using AdaCost as the boosting algorithm , and variations of PNrule and RIPPER as the base learners , we show that if algorithm A achieves better recall-precision balance than algorithm B , then using A as the base learner in AdaCost yields significantly better performance than using B as the base learner .
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