Comparative Analysis of CatBoost, Random Forest, and XGBoost for Predicting Student Academic Performance
Predicting student academic performance has become an important topic in educational data mining because it enables educational institutions to identify students who may require academic support at an early stage. This study compares the performance of three ensemble learning algorithms—CatBoost, Random Forest, and XGB...