Provable guarantees for decision tree induction: the agnostic setting

ICML 2020 Guy BlancJane LangeLi-Yang Tan

We give strengthened provable guarantees on the performance of widely employed and empirically successful {\sl top-down decision tree learning heuristics}. While prior works have focused on the realizable setting, we consider the more realistic and challenging {\sl agnostic} setting... (read more)

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