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Robustness to distribution shift has become a growing concern for text andimage models as they transition from research subjects to deployment in thereal world. However, high-quality benchmarks for distribution shift in tabularmachine learning tasks are still lacking despite the widespread real-world useof tabular data and differences in the models used for tabular data incomparison to text and images. As a consequence, the robustness of tabularmodels to distribution shift is poorly understood. To address this issue, weintroduce TableShift, a distribution shift benchmark for tabular data.TableShift contains 15 binary classification tasks in total, each with anassociated shift, and includes a diverse set of data sources, predictiontargets, and distribution shifts. The benchmark covers domains includingfinance, education, public policy, healthcare, and civic participation, and isaccessible using only a few lines of Python code via the TableShift API. Weconduct a large-scale study comparing several state-of-the-art tabular datamodels alongside robust learning and domain generalization methods on thebenchmark tasks. Our study demonstrates (1) a linear trend betweenin-distribution (ID) and out-of-distribution (OOD) accuracy; (2) domainrobustness methods can reduce shift gaps but at the cost of reduced IDaccuracy; (3) a strong relationship between shift gap (difference between IDand OOD performance) and shifts in the label distribution.  The benchmark data, Python package, model implementations, and moreinformation about TableShift are available athttps://github.com/mlfoundations/tableshift and https://tableshift.org .

Benchmarking Distribution Shift in Tabular Data with TableShift