Summary of The Role Of Depth, Width, and Tree Size in Expressiveness Of Deep Forest, by Shen-huan Lyu et al.
The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest
by Shen-Huan Lyu, Jin-Hui Wu, Qin-Cheng Zheng, Baoliu Ye
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 the theoretical explanation of deep forest algorithms, a variant of random forests that uses multi-layer forests to improve performance. Building on previous work by Zhou et al. (2019), this study provides upper and lower bounds on the approximation complexity of deep forests in terms of three key hyperparameters: depth, width, and tree size. The results show that depth plays a crucial role in enhancing expressiveness, with exponential improvements possible compared to width and tree size. Empirical experiments validate these theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep forest algorithms are an improvement on traditional random forests, which combine multiple decision trees for better predictions. Researchers have explored ways to make deep forests work even better by adjusting three key settings: how many layers the algorithm uses, how many decisions each layer makes, and how big each tree is. This study helps us understand why these settings matter so much. It shows that having more layers can make a huge difference in what we can learn from data, whereas changing other settings doesn’t have as big an impact. |
