Summary of Scalable Rule Lists Learning with Sampling, by Leonardo Pellegrina and Fabio Vandin
Scalable Rule Lists Learning with Sampling
by Leonardo Pellegrina, Fabio Vandin
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach to generating optimal rule lists for interpretable machine learning models is proposed in this paper. Rule lists are a type of interpretable model that can be easily understood by humans, making them valuable for decision-making applications. However, finding the best possible rule list is a computationally challenging problem that has not been well addressed. Current methods are impractical for large datasets, limiting their usefulness. This paper presents a new method for finding optimal rule lists that addresses these challenges and shows promising results on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a way to make machine learning models easier to understand. They focused on “rule lists” which are models that humans can easily read and use in important decision-making situations. The problem is that making the best rule list is very hard for computers, especially when dealing with lots of data. This paper shares a new approach that helps computers create good rule lists quickly and efficiently. |
Keywords
» Artificial intelligence » Machine learning
