Enhancing Predictive Analytics with Gradient Boosting Machine: Insights from Tennis Match Prediction

This paper presents a comprehensive exploration of the Gradient Boosting Machine (GBM) technique, emphasizing its capabilities in predictive modeling. By using tennis match prediction as a case study, we demonstrate GBM's adeptness in handling complex datasets involving player performance, match statistics, and situational variables. The versatility and accuracy of GBM are evidenced through feature importance analysis and performance evaluation, highlighting its potential across various domains.