TabArena: Benchmarking Tabular Machine Learning with Reproducibility and Ensembling at Scale
Understanding the Importance of Benchmarking in Tabular ML Machine learning on tabular data focuses on building models that learn patterns from structured datasets, typically composed of rows and columns similar to those found in spreadsheets. These datasets are used in industries ranging from healthcare to finance, where accuracy and interpretability are essential. Techniques such as gradient-boosted trees and neural networks are commonly used, and recent advances have introduced foundation models designed to handle tabular data structures. Ensuring fair and effective comparisons between these methods has become increasingly important as new models continue to emerge. Challenges with Existing Benchmarks One challenge in this domain is that benchmarks for evaluating models on tabular data are often outdated or flawed. Many benchmarks continue to utilize obsolete datasets with licensing issues or those that do not accurately reflect real-world tabular use cases. Furthermore, some ben...