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.Comment: 17 pages, 8 figures, 9 tables, Presented at NVIDIA GTC, The
Conference for the Era of AI and the Metaverse, March 23, 2023. [S51129