<ul><li>MultiTab is a benchmark suite and evaluation framework designed for multi-dimensional analysis of tabular learning algorithms.</li><li>It categorizes 196 datasets based on key data characteristics and evaluates 13 models to understand how model behavior varies across diverse data regimes.</li><li>Model performance is shown to be highly sensitive to data regimes, with different models excelling based on factors like sample size and feature interaction.</li><li>MultiTab enables more informed model design and provides practical guidance for selecting models suited to specific data characteristics.</li></ul>

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains

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