Tabular Benchmark

Tabular benchmark research focuses on creating standardized datasets and evaluation frameworks for assessing machine learning models on tabular data, a ubiquitous data format across many domains. Current efforts concentrate on developing benchmarks that address limitations of existing ones, such as insufficient data scarcity representation, lack of temporal dynamics, and underrepresentation of real-world complexities like feature engineering and evolving data. This work is crucial for advancing the field by enabling more robust comparisons of different model architectures (including deep learning, tree-based methods, and LLMs) and algorithms, ultimately leading to improved model performance and more reliable deployment in practical applications.