# Analyzing the tree-layer structure of Deep Forests

arXiv: https://arxiv.org/abs/2010.15690 · HTML: https://mlsanity.com/paper/2010.15690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Deep Forest with Hashing Screening and Window Screening](https://mlsanity.com/paper/2207.11951/) (2022)
- [Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule](https://mlsanity.com/paper/2309.09030/) (2023)
- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)
- [The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest](https://mlsanity.com/paper/2407.05108/) (2024)
- [Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation](https://mlsanity.com/paper/2502.00465/) (2025)

## Abstract (arXiv)

Random forests on the one hand, and neural networks on the other hand, have met great success in the machine learning community for their predictive performance. Combinations of both have been proposed in the literature, notably leading to the so-called deep forests (DF) (Zhou & Feng,2019). In this paper, our aim is not to benchmark DF performances but to investigate instead their underlying mechanisms. Additionally, DF architecture can be generally simplified into more simple and computationally efficient shallow forest networks. Despite some instability, the latter may outperform standard predictive tree-based methods. We exhibit a theoretical framework in which a shallow tree network is shown to enhance the performance of classical decision trees. In such a setting, we provide tight theoretical lower and upper bounds on its excess risk. These theoretical results show the interest of tree-network architectures for well-structured data provided that the first layer, acting as a data encoder, is rich enough.