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Decision Tree Heuristics Can Fail, Even in the Smoothed Setting

Authors Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan

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Author Details

Guy Blanc
  • Stanford University, CA, USA
Jane Lange
  • Massachusetts Institute of Technology, Cambridge, MA, USA
Mingda Qiao
  • Stanford University, CA, USA
Li-Yang Tan
  • Stanford University, CA, USA

Funding

  • Lange, Jane: NSF Award #CCF-2006664

Acknowledgements

We thank the anonymous reviewers, whose suggestions have helped improved this paper.

Cite AsGet BibTex

Guy Blanc, Jane Lange, Mingda Qiao, and Li-Yang Tan. Decision Tree Heuristics Can Fail, Even in the Smoothed Setting. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 207, pp. 45:1-45:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2021.45

Abstract

Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long been known that there are simple target functions for which they fail badly (Kearns and Mansour, STOC 1996). Recent work of Brutzkus, Daniely, and Malach (COLT 2020) considered the smoothed analysis model as a possible avenue towards resolving this disconnect. Within the smoothed setting and for targets f that are k-juntas, they showed that these heuristics successfully learn f with depth-k decision tree hypotheses. They conjectured that the same guarantee holds more generally for targets that are depth-k decision trees. We provide a counterexample to this conjecture: we construct targets that are depth-k decision trees and show that even in the smoothed setting, these heuristics build trees of depth 2^{Ω(k)} before achieving high accuracy. We also show that the guarantees of Brutzkus et al. cannot extend to the agnostic setting: there are targets that are very close to k-juntas, for which these heuristics build trees of depth 2^{Ω(k)} before achieving high accuracy.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • decision trees
  • learning theory
  • smoothed analysis

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