Summary of Adaptive Split Balancing For Optimal Random Forest, by Yuqian Zhang et al.
Adaptive Split Balancing for Optimal Random Forest
by Yuqian Zhang, Weijie Ji, Jelena Bradic
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
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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 A novel random forest algorithm, adaptive split-balancing forest (ASBF), is proposed to construct trees using a permutation-based balanced splitting criterion. This approach achieves minimax optimality under the Lipschitz class, while its localized version attains the minimax rate under the Hölder class of problems. The proposed method is shown to be optimal in simple, smooth scenarios and adaptively learns the tree structure from data. Additionally, it establishes uniform upper bounds and demonstrates improved dimensionality dependence in average treatment effect estimation problems. The algorithm’s performance is superior to existing random forests in simulation studies and real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of building trees for machine learning is introduced. This method uses a different approach than what’s commonly used, which can sometimes make the results worse. The new method does better in many situations and can learn from the data it’s working with. It also works well with complex problems where there are many variables to consider. |
Keywords
* Artificial intelligence * Machine learning * Random forest
