PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
This addresses a problem for researchers and practitioners in data-scarce applications by providing a benchmark to evaluate method efficiency, though it is incremental as it adapts existing datasets for a specific regime.
The authors tackled the lack of benchmarks for small-sized tabular data by introducing PMLBmini, a suite of 44 binary classification datasets with sample sizes ≤ 500, and found that state-of-the-art AutoML and deep learning methods often fail to outperform simple logistic regression in low-data regimes, though they identified scenarios where these methods are reasonable.
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.