TALENT: A Tabular Analytics and Learning Toolbox

Understand

Tabular data is one of the most common data sources in machine learning.

• Although a wide range of classical methods demonstrate practical utilities in this field, deep learning methods on tabular data are becoming promising alternatives due to their flexibility and ability to capture complex interactions within the data.
• Considering that deep tabular methods have diverse design philosophies, including the ways they handle features, design learning objectives, and construct model architectures, we introduce a versatile deep-learning toolbox called TALENT (Tabular Analytics and LEarNing Toolbox) to utilize, analyze, and compare tabular methods.
• TALENT encompasses an extensive collection of more than 20 deep tabular prediction methods, associated with various encoding and normalization modules, and provides a unified interface that is easily integrable with new methods as they emerge.