Summary of A Survey on Data Quality Dimensions and Tools For Machine Learning, by Yuhan Zhou et al.
A Survey on Data Quality Dimensions and Tools for Machine Learning
by Yuhan Zhou, Fengjiao Tu, Kewei Sha, Junhua Ding, Haihua Chen
First submitted to arxiv on: 28 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 The abstract discusses the importance of data quality (DQ) in machine learning (ML), highlighting its critical role in ensuring the performance, fairness, robustness, safety, and scalability of ML models. To address the challenges faced by traditional methods like exploratory data analysis (EDA) and cross-validation (CV), the authors review 17 DQ evaluation and improvement tools developed over the past five years. The survey compares the strengths and limitations of these tools, proposing a roadmap for developing open-source DQ tools for ML. Furthermore, the abstract highlights the potential applications of large language models (LLMs) and generative AI in DQ evaluation and improvement for ML. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how important it is to have good quality data when using machine learning. Good data makes sure that the machine learning models work well, are fair, safe, and can handle lots of information. The authors look at 17 tools that help check and improve the quality of data, and they compare their strengths and weaknesses. They also talk about how new types of AI, like language models, can be used to make sure the data is good. |
Keywords
» Artificial intelligence » Machine learning
