<ul><li>Tabular data has gained attention in machine learning research, with the deep learning model TabPFN v2 showing promising performance and scalability potential.</li><li>Research on improving TabPFN v2 performance has mostly focused on closed environments, neglecting challenges in open environments.</li><li>A comprehensive evaluation was conducted to assess TabPFN v2's adaptability in open environments, revealing limitations but suitability for specific tasks.</li><li>Tree-based models are still preferred for general tabular tasks in open environments, and the need for open environments tabular benchmarks and model robustness enhancements was emphasized.</li></ul>

Realistic Evaluation of TabPFN v2 in Open Environments

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