Summary of Matching Problems to Solutions: An Explainable Way Of Solving Machine Learning Problems, by Lokman Saleh et al.
Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems
by Lokman Saleh, Hafedh Mili, Mounir Boukadoum, Abderrahmane Leshob
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 machine learning-based problem-solving framework is proposed to aid non-experts in applying ML techniques to their domain-specific problems. The framework involves three steps: formulating a business problem as a data analysis problem, sketching an ML-based solution pattern, and designing/refining the solution components. The goal is to capture a body of ML problem-solving knowledge and embody it in a workbench that facilitates exploration of the ML solution space. This paper focuses on representing domain problems, ML problems, and ML solution artefacts, as well as developing a heuristic matching function to identify the most suitable ML algorithm family for a given domain problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of experts is working together with data scientists to solve real-world problems using machine learning techniques. They’re creating a special tool that helps people who aren’t experts in machine learning to use these techniques to find solutions to their own problems. This paper talks about how they’re designing this tool and what it will do. |
Keywords
* Artificial intelligence * Machine learning
