  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. In
this paper, we present the design and functionality of the toolbox, illustrate
its practical application through several case studies, and investigate the
performance of various methods fairly based on our toolbox. Code is available
at https://github.com/qile2000/LAMDA-TALENT.
