Karya Suhada

Explainable Machine Learning Pipeline for Early Warning of Student Dropout in Graduate Programs Using SHAP and Gradient Boosting

Student attrition in graduate programs is costly for students and institutions alike, yet most predictive models deployed for early warning behave as black boxes that program managers cannot audit. This paper proposes an end-to-end explainable machine learning pipeline for the early detection of dropout risk in master’...