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TabArena: A Living Benchmark for Machine Learning on Tabular Data
Created by
Haebom
Author
Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmuller , Prateek Mutalik Desai, David Salinas, Frank Hutter
Outline
TabArena is the first dynamic, continuously maintained tabular data benchmarking system. To overcome the limitations of existing benchmarking systems that are static and not updated, we collected various datasets and excellent models and built a public leaderboard through a large-scale benchmarking study. This study highlights the impact of validation methods and ensemble hyperparameter configurations on model performance evaluation, and shows that deep learning methods can approach or outperform gradient boosting trees using large-scale time allocation and ensemble techniques. We also analyze the state-of-the-art performance improvements through ensemble techniques in tabular data learning and the contributions of individual models, and release TabArena ( https://tabarena.ai) along with a public leaderboard, reproducible code, and maintenance protocol.
Takeaways, Limitations
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Takeaways:
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We have established a standardized platform for comparing the performance of tabular data models by providing a dynamic benchmarking system that is continuously updated.
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We found that validation methods and hyperparameter ensembles have a significant impact on model performance evaluation.
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We demonstrate that deep learning methods achieve comparable performance to gradient boosting trees using large-scale time allocation and ensemble techniques.
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On small datasets, the base model performs well.
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We demonstrate that model ensembles improve state-of-the-art performance on tabular data machine learning.
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Limitations:
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Additional validation may be needed to confirm the representativeness of the dataset and models presented in this study.
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As new models and datasets continue to emerge, TabArena requires ongoing maintenance and updates.
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More comprehensive benchmarking of different types of tabular data may be needed.
