  Tabular data is prevalent in real-world machine learning applications, and
new models for supervised learning of tabular data are frequently proposed.
Comparative studies assessing the performance of models typically consist of
model-centric evaluation setups with overly standardized data preprocessing.
This paper demonstrates that such model-centric evaluations are biased, as
real-world modeling pipelines often require dataset-specific preprocessing and
feature engineering. Therefore, we propose a data-centric evaluation framework.
We select 10 relevant datasets from Kaggle competitions and implement
expert-level preprocessing pipelines for each dataset. We conduct experiments
with different preprocessing pipelines and hyperparameter optimization (HPO)
regimes to quantify the impact of model selection, HPO, feature engineering,
and test-time adaptation. Our main findings are: 1. After dataset-specific
feature engineering, model rankings change considerably, performance
differences decrease, and the importance of model selection reduces. 2. Recent
models, despite their measurable progress, still significantly benefit from
manual feature engineering. This holds true for both tree-based models and
neural networks. 3. While tabular data is typically considered static, samples
are often collected over time, and adapting to distribution shifts can be
important even in supposedly static data. These insights suggest that research
efforts should be directed toward a data-centric perspective, acknowledging
that tabular data requires feature engineering and often exhibits temporal
characteristics.
