Summary of The Representation Of Meaningful Precision, and Accuracy, by a Mani
The Representation of Meaningful Precision, and Accuracy
by A Mani
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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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 abstract discusses the limitations of traditional precision and accuracy measures in machine learning and statistical learning. It highlights that these metrics are often problem-dependent and do not provide meaningful insights into a model’s relevance or patterns. The authors argue that existing measures are insufficient for understanding cognition domains, where analogous concepts are lacking. In response, they propose a compositional knowledge representation approach within a minimalist general framework, aiming to address key issues and cover most application contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research explores the challenges of using precision and accuracy metrics in machine learning and statistical learning. It shows that these measures are not always relevant or meaningful, especially when dealing with different problem types or cognition domains. To overcome these limitations, the authors suggest a new approach to knowledge representation that can be applied across various contexts. |
Keywords
* Artificial intelligence * Machine learning * Precision
