Comparitive Analysis of Gradient Boosting and Transformer Based Models for Binary Classification in Tabular Data
Jebaraj Vasudevan · 2025
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.