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TALENT: A Tabular Analytics and Learning Toolbox

Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, Han-Jia Ye

TL;DR

TALENT introduces a unified, extensible toolbox for tabular data prediction that integrates classical, tree-based, and deep tabular methods within a single interface. It provides standardized encoding modules, config-driven hyperparameter tuning via Optuna, and a reproducible workflow to fairly compare diverse methods. The paper details the toolbox architecture, workflow, and procedures for adding new methods, plus preliminary experiments showing competitive performance across binary, multiclass, and regression tasks. The practical impact lies in enabling researchers and practitioners to select, compare, and extend deep tabular models more efficiently, with transparent preprocessing and evaluation.

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.

TALENT: A Tabular Analytics and Learning Toolbox

TL;DR

TALENT introduces a unified, extensible toolbox for tabular data prediction that integrates classical, tree-based, and deep tabular methods within a single interface. It provides standardized encoding modules, config-driven hyperparameter tuning via Optuna, and a reproducible workflow to fairly compare diverse methods. The paper details the toolbox architecture, workflow, and procedures for adding new methods, plus preliminary experiments showing competitive performance across binary, multiclass, and regression tasks. The practical impact lies in enabling researchers and practitioners to select, compare, and extend deep tabular models more efficiently, with transparent preprocessing and evaluation.

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.
Paper Structure (13 sections, 1 equation, 4 figures, 1 table)

This paper contains 13 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Talent for Tabular Prediction
  3. Preliminary
  4. Supported Methods
  5. Encoding Techniques
  6. Toolbox Usage
  7. Dependencies
  8. The workflow of Talent
  9. Model Default Hyperparameters and Search Space
  10. Example Usage
  11. Adding New Methods
  12. Preliminary Experiments
  13. Conclusion

Figures (4)

  • Figure 1: Various deep prediction methods for tabular data in Talent.
  • Figure 2: Flowchart depicting the data prediction process with Talent.
  • Figure 3: Workflow for Adding a New Method to Talent.
  • Figure 4: Performance-Efficiency-Size comparison of representative tabular methods on our toolbox for (a) binary classification, (b) multi-class classification, (c) regression tasks, and (d) all task types. The performance is measured by the average rank of all methods (lower is better). We also consider the dummy baseline, which outputs the label of the major class and the average labels for classification and regression tasks, respectively.