On the Adaptive Properties of Decision Trees
Abstract
Decision trees are surprisingly adaptive in three important respects: They automatically (1) adapt to favorable conditions near the Bayes decision boundary; (2) focus on data distributed on lower dimensional manifolds; (3) reject irrelevant features. In this paper we examine a decision tree based on dyadic splits that adapts to each of these conditions to achieve minimax optimal rates of convergence. The proposed classifier is the first known to achieve these optimal rates while being practical and im- plementable.
Cite
Text
Scott and Nowak. "On the Adaptive Properties of Decision Trees." Neural Information Processing Systems, 2004.Markdown
[Scott and Nowak. "On the Adaptive Properties of Decision Trees." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/scott2004neurips-adaptive/)BibTeX
@inproceedings{scott2004neurips-adaptive,
title = {{On the Adaptive Properties of Decision Trees}},
author = {Scott, Clayton and Nowak, Robert},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1225-1232},
url = {https://mlanthology.org/neurips/2004/scott2004neurips-adaptive/}
}