Summary of Exogenous Randomness Empowering Random Forests, by Tianxing Mei et al.
Exogenous Randomness Empowering Random Forests
by Tianxing Mei, Yingying Fan, Jinchi Lv
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 This paper investigates how external randomness affects the effectiveness of random forests with tree-building rules that are not influenced by training data. The authors introduce the concept of exogenous randomness, which is present in two forms: Type I from feature subsampling and Type II from tie-breaking during tree-building processes. They develop non-asymptotic expansions for mean squared error (MSE) for individual trees and forests, and establish sufficient and necessary conditions for consistency. These findings are further explored through simulations, revealing that feature subsampling reduces bias and variance in random forests compared to individual trees, serving as an adaptive mechanism. Additionally, the authors discover that noisy features can “bless” random forest performance by introducing randomness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how adding extra randomness helps or hurts the accuracy of a type of machine learning model called random forests. The researchers define what they mean by “extra randomness” and show that it comes in two forms: one from choosing which features to use, and another from deciding which tree-building rule to follow. They also find ways to predict how well these models will do using math formulas. The authors test their ideas with computer simulations and discover that adding extra randomness can actually make the models better by balancing out mistakes they might make. |
Keywords
» Artificial intelligence » Machine learning » Mse » Random forest
