Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions

TL;DR

This paper investigates the effectiveness of class-balanced loss functions in improving Gradient Boosting Decision Trees (GBDT) performance on imbalanced tabular datasets through extensive experiments and provides a practical Python package for implementation.

Contribution

It is the first comprehensive empirical study on adapting class-balanced loss functions to GBDT models for various classification tasks, including benchmarking and tool development.

Findings

Class-balanced losses improve GBDT performance on imbalanced datasets.

The study provides a benchmark across multiple datasets and GBDT algorithms.

A Python package is introduced to facilitate adoption of these techniques.

Abstract

Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks. While Gradient Boosting Decision Trees (GBDT) models have proven highly effective for such tasks, their performance can be compromised when dealing with imbalanced datasets. This paper presents the first comprehensive study on adapting class-balanced loss functions to three GBDT algorithms across various tabular classification tasks, including binary, multi-class, and multi-label classification. We conduct extensive experiments on multiple datasets to evaluate the impact of class-balanced losses on different GBDT models, establishing a valuable benchmark. Our results demonstrate the potential of class-balanced loss functions to enhance GBDT performance on imbalanced datasets, offering a robust approach for practitioners facing class imbalance challenges in real-world applications. Additionally, we introduce a Python package that facilitates the integration of class-balanced loss functions into GBDT workflows, making these advanced techniques accessible to a wider audience.