What is TabNet?

TabNet is a neural network architecture specifically designed for working with tabular data. Unlike traditional models such as gradient boosting, TabNet uses an attention mechanism to select the most important features at each training step.

Key features of TabNet:

• Uses sequential feature selection steps with attention masks, which improves model interpretability.
• Allows efficient training on tabular data without the need for manual feature selection.
• Provides competitive prediction quality compared to boosting methods, while maintaining the advantages of neural networks.

It is used in classification and regression tasks on tabular data, where both accuracy and interpretability of the model are important.