Summary of Can a Single Tree Outperform An Entire Forest?, by Qiangqiang Mao et al.
Can a Single Tree Outperform an Entire Forest?
by Qiangqiang Mao, Yankai Cao
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a framework for optimizing decision trees, which challenges the common assumption that single decision trees underperform random forests in terms of testing accuracy. The proposed approach, called gradient-based entire tree optimization, reformulates tree training as a differentiable unconstrained optimization task and employs a scaled sigmoid approximation strategy to improve performance. The authors also introduce an algorithmic scheme to reduce numerical instability and a subtree polish strategy to minimize approximation errors. Experimental results on 16 datasets show that the optimized tree outperforms random forests by an average of 2.03% in testing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making decision trees better at predicting things correctly. Right now, most people think that using many small trees (called random forests) is better than using one big tree. But this study shows that if you make the big tree really good at its job, it can be just as accurate as the random forest! The scientists used a new way of training the tree to make it work better. They also came up with some clever tricks to stop the computer from getting stuck or making mistakes. After testing their ideas on many different datasets, they found that their optimized tree was actually 2.03% better at predicting things than the classic random forest. |
Keywords
» Artificial intelligence » Optimization » Random forest » Sigmoid
