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When does Subagging Work?
April 3, 2024, 4:42 a.m. | Christos Revelas, Otilia Boldea, Bas J. M. Werker
cs.LG updates on arXiv.org arxiv.org
Abstract: We study the effectiveness of subagging, or subsample aggregating, on regression trees, a popular non-parametric method in machine learning. First, we give sufficient conditions for pointwise consistency of trees. We formalize that (i) the bias depends on the diameter of cells, hence trees with few splits tend to be biased, and (ii) the variance depends on the number of observations in cells, hence trees with many splits tend to have large variance. While these statements …
abstract arxiv bias cells cs.lg machine machine learning non-parametric parametric popular regression stat.ml study trees type work
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