PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
<|reference_start|>PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications: In practice, we are often faced with small-sized tabular data. However, current tabular benchmarks are not geared towards data-scarce applications, making it very difficult to derive meaningful conclusions from empirical comparisons. We introduce PMLBmini, a tabular benchmark suite of 44 binary classification datasets with sample sizes $\leq$ 500. We use our suite to thoroughly evaluate current automated machine learning (AutoML) frameworks, off-the-shelf tabular deep neural networks, as well as classical linear models in the low-data regime. Our analysis reveals that state-of-the-art AutoML and deep learning approaches often fail to appreciably outperform even a simple logistic regression baseline, but we also identify scenarios where AutoML and deep learning methods are indeed reasonable to apply. Our benchmark suite, available on https://github.com/RicardoKnauer/TabMini , allows researchers and practitioners to analyze their own methods and challenge their data efficiency.<|reference_end|>
@article{knauer2024pmlbmini:,
    title={PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce
  Applications},
    author={Ricardo Knauer, Marvin Grimm, Erik Rodner},
    journal={arXiv preprint arXiv:2409.01635},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01635},
    primaryClass={cs.LG cs.AI}
}