An innovative framework for student adaptability prediction using discriminant-based classification, feature selection, and sensitivity analysis
Accurately predicting student adaptability is essential for enhancing academic performance and personal development. The present study presents a classification-based approach utilizing Quadratic Discriminant Analysis (QDA), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA) to predict student...