Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths
Read the original on arXiv Machine Learning →The paper argues that typical tabular machine learning benchmarks, which aggregate results by averaging scores or ranks, can hide which models are essential for achieving the best performance on specific datasets. It proposes evaluating models against a data‑centric peak performance frontier, classifying them as irreplaceable, sufficient, redundant, or fallible based on their position relative to other models. Applying this to the TabArena benchmark shows that common aggregation metrics mainly capture consistency and failure avoidance, but fail to reflect dataset‑specific strengths, leading to a misalignment between aggregate rewards and true model utility.
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