Summary of Towards Modeling Data Quality and Machine Learning Model Performance, by Usman Anjum et al.
Towards Modeling Data Quality and Machine Learning Model Performance
by Usman Anjum, Chris Trentman, Elrod Caden, Justin Zhan
First submitted to arxiv on: 8 Dec 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 novel machine learning approach is introduced to quantify uncertainties and noise in data on models, ensuring accurate trust and performance measurements. The study proposes the deterministic-non-deterministic ratio (DDR) metric, which combines signal-to-noise ratio (SNR) principles to evaluate model performance. Experimental results using synthetic datasets demonstrate how accuracy changes with DDR, enabling the creation of DDR-accuracy curves for determining model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models need to be trusted and their performance measured accurately. A new way to do this is by understanding how uncertainty and noise in data affect models. The researchers propose a new metric called deterministic-non-deterministic ratio (DDR) that helps measure model performance. They use synthetic data to test their idea and show how accuracy changes with DDR. This can help determine if a model is performing well or not. |
Keywords
» Artificial intelligence » Machine learning » Synthetic data
