arXiv:2605.28418v1 Announce Type: new Abstract: With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult. We investigate whether dataset meta-features can explain performance gaps between model families on tabular prediction tasks. Using the TabArena benchmark results, we analyze dataset-level performance gaps and relate them to model-agnostic dataset descriptors. After strict statistical tests with false discovery control, we find that (1) for neural network vs. tree gaps, no m
The paper is a new arXiv publication, reflecting ongoing research into model selection for tabular data, a critical area given the proliferation of AI models.
Understanding why certain models perform better on specific tabular datasets allows for more efficient resource allocation and better predictive performance across industries relying on such data.
The ability to use meta-features to explain performance gaps could lead to more robust model selection processes, reducing trial-and-error in data science workflows.
- · Data scientists
- · ML platform providers
- · Industries with tabular data (finance, healthcare)
- · Inefficient model selection methods
- · Overly complex model architectures for simple tasks
Improved automated machine learning (AutoML) tools for tabular data.
Reduced computational costs and energy consumption in model development due to better initial model choices.
Accelerated deployment of AI solutions across various sectors by minimizing model selection overhead.
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Read at arXiv cs.LG