TabNet: Attentive Interpretable Tabular Learning
This work addresses the challenge of interpretable and efficient deep learning for tabular data, which is crucial for domains like healthcare and finance, though it builds on existing attention mechanisms.
The authors tackled the problem of learning from tabular data by proposing TabNet, a deep learning architecture that uses sequential attention to select features at each step, achieving state-of-the-art performance on various datasets and enabling interpretability through feature attributions.
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 neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior. Finally, for the first time to our knowledge, we demonstrate self-supervised learning for tabular data, significantly improving performance with unsupervised representation learning when unlabeled data is abundant.