Provable guarantees for decision tree induction: the agnostic setting

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 parameters $s\in \mathbb{N}$, these heuristics construct a decision tree of size $s^{\tilde{O}((\log s)/\varepsilon^2)}$ that achieves error $\le \mathsf{opt}_s + \varepsilon$, where $\mathsf{opt}_s$ 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 $s^{\tilde{\Omega}(\log s)}$ lower bound.