Summary of Optimal Sparse Regression Trees, by Rui Zhang et al.
Optimal Sparse Regression Trees
by Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin
First submitted to arxiv on: 28 Nov 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a new approach to constructing provably-optimal sparse regression trees using dynamic programming with bounds. This method leverages a novel lower bound based on the k-Means clustering algorithm in 1-dimension over the set of labels, enabling the construction of optimal sparse trees quickly, even for large and highly-correlated datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making AI models called regression trees better. It’s important because these models can be used to make predictions without needing a computer, which makes them useful in high-stakes situations. The problem is that nobody has tried to fully optimize these models before because it’s very hard to do. This new approach uses a special way of solving a math problem called k-Means clustering to help find the best model quickly. |
Keywords
* Artificial intelligence * Clustering * K means * Regression
