Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
It addresses class imbalance challenges for practitioners using GBDT models in real-world applications, but is incremental as it focuses on adapting existing loss functions.
This paper tackles the problem of class imbalance in tabular data classification by studying the adaptation of class-balanced loss functions to Gradient Boosting Decision Tree (GBDT) algorithms, demonstrating their potential to enhance performance across various classification tasks.
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.