PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications

• URL: http://arxiv.org/abs/2409.01635v1
• Date: Tue, 3 Sep 2024 06:13:03 GMT
• Title: PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
• Authors: Ricardo Knauer, Marvin Grimm, Erik Rodner,
• Abstract summary: PMLBmini is a 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. Our analysis reveals that state-of-the-art AutoML and deep learning approaches often fail to appreciably outperform even a simple logistic regression baseline.
• Score: 2.3700911865675187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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