# Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection

arXiv: https://arxiv.org/abs/2303.15218 · HTML: https://mlsanity.com/paper/2303.15218/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)

## Built on by (most cited first, 4 of 4)

- [Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment](https://mlsanity.com/paper/2504.18133/) (2025)
- [Optical Ocean Recipes: Creating Realistic Datasets to Facilitate Underwater Vision Research](https://mlsanity.com/paper/2509.20171/) (2025)
- [Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset](https://mlsanity.com/paper/2310.10670/) (2023)
- [Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations](https://mlsanity.com/paper/2605.22904/) (2026)

## Abstract (arXiv)

This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evaluation, as it stands out in several benchmarks due to its detection performance and speed. After introducing the problem of fraud detection, the paper reviews evaluation metrics for detection systems or binary classifiers, and illustrates with examples how different metrics work for balanced and imbalanced datasets. Then, it examines the principles of XGBoost. It proposes a pipeline for data preparation and compares a Vanilla XGBoost against a random search-tuned XGBoost. Random search fine-tuning provides consistent improvement for large datasets of 100 thousand samples, not so for medium and small datasets of 10 and 1 thousand samples, respectively. Besides, as expected, XGBoost recognition performance improves as more data is available, and deteriorates detection performance as the datasets become more imbalanced. Tests on distributions with 50, 45, 25, and 5 percent positive samples show that the largest drop in detection performance occurs for the distribution with only 5 percent positive samples. Sampling to balance the training set does not provide consistent improvement. Therefore, future work will include a systematic study of different techniques to deal with data imbalance and evaluating other approaches, including graphs, autoencoders, and generative adversarial methods, to deal with the lack of labels.