Authors : Jebaraj Vasudevan

Volume/Issue : Volume 10 - 2025, Issue 3 - March

---

Google Scholar : https://tinyurl.com/3x3p86xk

Scribd : https://tinyurl.com/53x4mxx2

DOI : https://doi.org/10.38124/ijisrt/25mar416

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

---

Abstract : This study compares the classification performance of the Gradient Boosting (XGBoost), and Transformer based model with multi-head self-attention for Tabular Data. While the methods exhibit broadly similar performance, the Transformer model particularly excels in Recall by about 8% showing that it would be better suited to applications such as Fraud Detection in Payment processing and Medical Diagnostics.

Keywords : Transformer, Gradient Boosting, XGBoost, Tabular Data.

References :

1. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," 2016.
2. X. Huang, A. Khetan, M. Cvitkovic and Z. Karnin, "TabTransformer: Tabular Data Modeling Using Contextual Embeddings," 2020.
3. "Kaggle," [Online]. Available: https://www.kaggle.com/datasets/blastchar/telco-customer-churn/data.
4.  A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones and A. Gomez, "Attention is all you need," 2017.