Open access
Oct 2026
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...
Li-Jun Mao, K. Alkayid, H. S. Shreenidhi et al.
· Scientific Reports · 0 citations