Summary of Riemann-lebesgue Forest For Regression, by Tian Qin et al.
Riemann-Lebesgue Forest for Regression
by Tian Qin, Wei-Min Huang
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposes a novel ensemble method called Riemann-Lebesgue Forest (RLF) for regression, which mimics the way measurable functions are approximated by partitioning their range into intervals. The core idea is to develop a new tree learner, Riemann-Lebesgue Tree (RLT), that performs Lebesgue-type cutting, reducing variance in response Y. The optimal cutting results in larger variance reduction than ordinary CART cutting, benefiting the ensemble part of RLF. The paper also generalizes asymptotic normality under different parameter settings and demonstrates competitive performance against random forest on simulation data and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict numbers called Riemann-Lebesgue Forest (RLF). It’s like taking a big number and breaking it into smaller parts to make it easier to understand. The new method, called Riemann-Lebesgue Tree, is better than what people used before because it makes the predictions more accurate. The researchers tested their method on fake data and real-life data from places like hospitals and found that it works well. |
Keywords
* Artificial intelligence * Random forest * Regression
