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. Our framework is available under: https://github.com/atschalz/dc_tabeval.
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
University of Kansas（堪萨斯大学）；Southwestern University of Finance and Economics（西南财经大学）；Arizona State University（亚利桑那州立大学）；Portland State University（波特兰州立大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Macau（澳门大学）；The Hong Kong University of Science and Technology（香港科技大学）
DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
Department of Computer Science and Artificial Intelligence, University of Granada（格拉纳达大学计算机科学与人工智能系）；Andalusian Institute of Data Science and Computational Intelligence (DaSCI), University of Granada（安达卢西亚数据科学与计算智能研究所（DaSCI），格拉纳达大学）
A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspective
Department of Computer and Data Sciences（计算机与数据科学系）；Case Western Reserve University（凯斯西储大学）；Department of Computer Science & Engineering（计算机科学与工程系）；Texas A&M University（德克萨斯大学）；Department of Biochemistry（生物化学系）；Center for RNA Science and Therapeutics（RNA科学与治疗中心）；Department of Biomedical Engineering（生物医学工程系）
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Healthy Lifespan Institute, School of Computer Science, University of Sheffield（健康寿命研究所，计算机科学学院，谢菲尔德大学）；School of Electrical and Electronic Engineering, University of Sheffield（电子与电气工程学院，谢菲尔德大学）；Healthy Lifespan Institute, School of Sociological Studies, Politics and International Relations, University of Sheffield（健康寿命研究所，社会科学学院，政治与国际关系，谢菲尔德大学）；Digital Environment Research Institute, Queen Mary University of London（数字环境研究 institutes，伦敦女王大学）
Understanding the Surprising Generalization Properties of Tabular Foundation Models
Polytechnique Montréal（蒙特利尔理工学院）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Chandar Research Lab（钱达尔研究实验室）；University of Toronto（多伦多大学）；Layer 6 AI（第六层人工智能公司）；Prior Labs（普里奥实验室）；ELLIS Institute Tübingen（埃利斯研究所图宾根分部）；University of Freiburg（弗赖堡大学）；Cohere（科here公司）
Evaluating quality in synthetic data generation for large tabular health datasets
Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute（公共健康人工智能研究中心，德国罗伯特· Koch研究所）；German Centre for Cancer Registry Data, Robert Koch Institute（癌症登记数据德国中心，德国罗伯特· Koch研究所）；Medical Informatics, Berlin Institute of Health at Charité - Universitätsmedizin Berlin（医学信息学，柏林健康研究所（查理大学柏林医学中心））
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy
Renmin University of China（中国人民大学）；Cerebras Systems, Inc.（赛博拉斯系统公司）；National University of Singapore（新加坡国立大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
ZAYAN: Disentangled Contrastive Transformer for Tabular Remote Sensing Data
Lane Dept. of Computer Science and Electrical Engineering（兰恩计算机科学与电气工程系）；Dept. of Geology & Geography（地质与地理系）；West Virginia University（西弗吉尼亚大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Green University of Bangladesh（孟加拉国绿色大学）；Stamford University Bangladesh（班加罗尔斯坦福大学）
Beyond IID: How General Are Tabular Foundation Models, Really?
Prior Labs；University of Freiburg（弗赖堡大学）；University of Mannheim（曼海姆大学）；INRIA Saclay（法国国家信息与自动化研究所萨克雷研究中心）；Technion（技术学院）；ELLIS Institute Tübingen（图宾根ELLIS研究所）；Zuse School ELIZA（Zuse学校ELIZA）
Generalization Can Emerge in Tabular Foundation Models From a Single Table
University of Toronto（多伦多大学）；Polytechnique Montréal（蒙特利尔大学）；Mila – Quebec AI Institute（魁北克人工智能研究所）；Layer 6 AI（Layer 6 AI公司）；Prior Labs（Prior实验室）；ELLIS Institute（ELLIS研究所）；Tübingen University of Freiburg（弗赖堡大学）
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection
Sorbonne Paris Nord University（巴黎北大学）；Paris-Saclay University（巴黎萨克雷大学）；Groupe BPCE（BPCE集团）
