Summary of Improving Gbdt Performance on Imbalanced Datasets: An Empirical Study Of Class-balanced Loss Functions, by Jiaqi Luo et al.
Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
by Jiaqi Luo, Yuan Yuan, Shixin Xu
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the impact of class-balanced loss functions on Gradient Boosting Decision Trees (GBDT) models for tabular data classification tasks. The study focuses on adapting class-balanced losses to three GBDT algorithms across various classification tasks, including binary, multi-class, and multi-label classification. The authors conduct extensive experiments on multiple datasets to evaluate the effectiveness of class-balanced losses on different GBDT models, establishing a valuable benchmark for practitioners. The results demonstrate the potential of class-balanced loss functions to enhance GBDT performance on imbalanced datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Class imbalance in machine learning is a big problem! Researchers have found that Gradient Boosting Decision Trees (GBDT) models are great for certain tasks, but they can be bad at dealing with imbalanced data. This paper shows how using special loss functions can help GBDT models do better on these types of problems. |
Keywords
* Artificial intelligence * Boosting * Classification * Machine learning
