TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes

TL;DR

TILBench is a comprehensive benchmark evaluating over 40 algorithms across 57 datasets to understand their performance, robustness, and scalability in tabular imbalanced learning.

Contribution

It introduces TILBench, a large-scale empirical benchmark providing systematic comparisons of imbalanced learning methods across diverse data regimes.

Findings

No single method dominates across all settings.

Method effectiveness depends on dataset characteristics and computational constraints.

Practical recommendations are provided for method selection.

Abstract

Imbalanced learning remains a fundamental challenge in tabular data applications. Despite decades of research and numerous proposed algorithms, a systematic empirical understanding of how different imbalanced learning methods behave across diverse data characteristics is still lacking. In particular, it remains unclear how different method families compare in predictive performance, robustness under varying data characteristics, and computational scalability. In this work, we present Tabular Imbalanced Learning Benchmark (TILBench), a large-scale empirical benchmark for tabular imbalanced learning. TILBench evaluates more than 40 representative algorithms across 57 diverse tabular datasets, resulting in over 200000 controlled experiments across a wide range of data characteristics. Our findings show that no single method consistently dominates across all settings; instead, the effectiveness of imbalanced learning methods depends strongly on dataset characteristics and computational constraints. Based on these findings, we provide practical recommendations for selecting appropriate methods in real-world applications.