44个少样本表格数据集，专为低数据场景评估模型性能而设计。
PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
- 构建44个样本数≤500的二分类数据集，聚焦小样本场景
- 发现顶尖AutoML与深度学习在低数据下常不如逻辑回归基准
- 适合研究数据效率、小样本建模或验证新算法的研究者使用
实际应用中常面临小规模表格数据，但现有表格基准不适用于数据稀缺场景，导致实证比较难以得出有意义结论。本文提出PMLBmini，一个包含44个二分类数据集的表格基准套件，样本量均≤500。我们用该套件全面评估当前自动化机器学习（AutoML）框架、现成的表格深度神经网络及经典线性模型在低数据条件下的表现。分析表明，最先进的AutoML与深度学习方法在多数情况下无法显著优于简单的逻辑回归基线，但也识别出其适用的具体场景。该基准套件已开源（https://github.com/RicardoKnauer/TabMini），供研究人员和实践者评估自身方法并挑战数据效率极限。
原文摘要 · Abstract (English)
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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