Summary of Time to Retrain? Detecting Concept Drifts in Machine Learning Systems, by Tri Minh Triet Pham et al.
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
by Tri Minh Triet Pham, Karthikeyan Premkumar, Mohamed Naili, Jinqiu Yang
First submitted to arxiv on: 11 Oct 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 The paper proposes a novel model-agnostic technique called CDSeer for detecting concept drift in machine learning models. Concept drift occurs when there is a change in the underlying data distribution, causing previously trained models to perform poorly. The authors evaluate CDSeer on eight datasets from different domains and use cases, finding that it outperforms state-of-the-art semi-supervised methods while requiring significantly less manual labeling. The improved performance and ease of adoption make CDSeer valuable for making ML systems more reliable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CDSeer is a new way to detect changes in data so machine learning models don’t get stuck. When data changes, models can start making mistakes. CDSeer helps by finding these changes early on, which makes models better and easier to use. The team tested CDSeer on many different datasets and found it works well. It’s even better than some other methods that require a lot more work. |
Keywords
* Artificial intelligence * Machine learning * Semi supervised
