  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.
