Summary of When Does Subagging Work?, by Christos Revelas et al.
When does Subagging Work?
by Christos Revelas, Otilia Boldea, Bas J.M. Werker
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the effectiveness of subsample aggregating (subagging) on regression trees, a popular machine learning method. The study provides sufficient conditions for pointwise consistency of trees, showing that bias depends on cell diameter and variance on the number of observations. Subagging is found to improve upon single trees across different numbers of splits, but can be outperformed by optimally grown individual trees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how subagging affects regression trees in machine learning. Researchers identify conditions where subagging helps or hurts tree performance. They find that combining multiple trees reduces bias and variance, but also discover a counterintuitive result: single trees can sometimes perform better if they’re grown to the right size. |
Keywords
* Artificial intelligence * Machine learning * Regression
