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While deep learning has enabled tremendous progress on text and imagedatasets, its superiority on tabular data is not clear. We contribute extensivebenchmarks of standard and novel deep learning methods as well as tree-basedmodels such as XGBoost and Random Forests, across a large number of datasetsand hyperparameter combinations. We define a standard set of 45 datasets fromvaried domains with clear characteristics of tabular data and a benchmarkingmethodology accounting for both fitting models and finding goodhyperparameters. Results show that tree-based models remain state-of-the-art onmedium-sized data ($\sim$10K samples) even without accounting for theirsuperior speed. To understand this gap, we conduct an empirical investigationinto the differing inductive biases of tree-based models and Neural Networks(NNs). This leads to a series of challenges which should guide researchersaiming to build tabular-specific NNs: 1. be robust to uninformative features,2. preserve the orientation of the data, and 3. be able to easily learnirregular functions. To stimulate research on tabular architectures, wecontribute a standard benchmark and raw data for baselines: every point of a 20000 compute hours hyperparameter search for each learner.

Why do tree-based models still outperform deep learning on tabular data?