Summary of Pmlbmini: a Tabular Classification Benchmark Suite For Data-scarce Applications, by Ricardo Knauer et al.
PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
by Ricardo Knauer, Marvin Grimm, Erik Rodner
First submitted to arxiv on: 3 Sep 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 paper introduces PMLBmini, a suite of 44 binary classification datasets with sample sizes less than or equal to 500. This tabular benchmark is designed for evaluating machine learning models in small-sized data scenarios, which are common in practice. The authors use PMLBmini to compare the performance of current automated machine learning (AutoML) frameworks, off-the-shelf tabular deep neural networks, and classical linear models. The results show that state-of-the-art approaches often fail to outperform a simple logistic regression baseline, but there are scenarios where they are reasonable to apply. The authors also make their benchmark suite available on GitHub for others to use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special set of datasets that are small, with only 500 examples or less. This is important because many machine learning models don’t work well when you have very little data. The authors test some popular machine learning methods on these small datasets and find that even the best methods don’t do much better than a simple method called logistic regression. However, there are certain situations where using more advanced methods can be helpful. The authors share their dataset so that others can try out their own ideas. |
Keywords
» Artificial intelligence » Classification » Logistic regression » Machine learning
