Jul 12, 2020
We give strengthened provable guarantees on the performance of widely employed and empirically successful top-down decision tree learning heuristics. While prior works have focused on the realizable setting, we consider the more realistic and challenging agnostic setting. We show that for all monotone functions f and s∈, these heuristics construct a decision tree of size s^Õ((log s)/^2) that achieves error <_s +, where _s denotes the error of the optimal size-s decision tree for f. Previously such a guarantee was not known to be achievable by any algorithm, even one that is not based on top-down heuristics. We complement our algorithmic guarantee with a near-matching s^Ω̃(log s) lower bound.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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