TALENT: A Tabular Analytics and Learning Toolbox

TL;DR

TALENT is a comprehensive deep learning toolbox for tabular data that facilitates the comparison, analysis, and application of over 20 methods within a unified framework, promoting research and practical use.

Contribution

The paper introduces TALENT, a versatile and extensible toolbox that consolidates diverse deep tabular methods with a unified interface for easier analysis and comparison.

Findings

TALENT enables fair performance comparison of deep tabular methods.

The toolbox supports integration of new methods and modules.

Case studies demonstrate practical utility and flexibility.

Abstract

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.