Summary of Probabilistic Scoring Lists For Interpretable Machine Learning, by Jonas Hanselle et al.
Probabilistic Scoring Lists for Interpretable Machine Learning
by Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier
First submitted to arxiv on: 31 Jul 2024
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 A new scoring system, called probabilistic scoring lists (PSL), is proposed to make decisions more explainable and accurate. The PSL approach extends traditional scoring systems by incorporating uncertainty into the decision-making process through probability distributions or intervals. Unlike traditional scoring systems, which make deterministic decisions, PSLs evaluate features one by one and stop as soon as a confident decision can be made. This paper also presents a method for learning PSLs from data and conducts a case study in the medical domain to evaluate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to make decisions that’s more transparent and accurate. Instead of just saying yes or no, this system shows how sure it is about its answer. It works by looking at different features one by one and stopping when it’s confident enough to make a decision. This could be useful in places like hospitals where doctors need to make important decisions quickly. The team tested their idea in the medical field and found that it worked well. |
Keywords
* Artificial intelligence * Probability
