A Closer Look at Deep Learning Methods on Tabular Datasets

Understand

Tabular data is prevalent across diverse domains in machine learning.

• With the rapid progress of deep tabular prediction methods, especially pretrained (foundation) models, there is a growing need to evaluate these methods systematically and to understand their behavior.
• We present an extensive study on TALENT, a collection of 300+ datasets spanning broad ranges of size, feature composition (numerical/categorical mixes), domains, and output types (binary, multi--class, regression).
• Our evaluation shows that ensembling benefits both tree-based and neural approaches.