TabNet: Attentive Interpretable Tabular Learning

Authors

  • Sercan Ö. Arik Google
  • Tomas Pfister Google

Keywords:

Classification and Regression, Representation Learning, Semi-Supervised Learning, Unsupervised & Self-Supervised Learning

Abstract

We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into its global behavior. Finally, we demonstrate self-supervised learning for tabular data, significantly improving performance when unlabeled data is abundant.

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Published

2021-05-18

How to Cite

Arik, S. Ö., & Pfister, T. (2021). TabNet: Attentive Interpretable Tabular Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6679-6687. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16826

Issue

Section

AAAI Technical Track on Machine Learning I