Calibrated Probabilistic Stacking with Linear Meta-Learning for Admission Outcome Prediction

Abstract

This paper presents a two-stage probabilistic stacking ensemble for admission outcome prediction based on calibrated heterogeneous classifiers and linear decision fusion in probability space. The proposed approach combines HistGradientBoosting, ExtraTrees, and RandomForest models to generate posterior class probabilities, while probability calibration is applied to improve the consistency and comparability of probabilistic estimates. To prevent information leakage, the meta-level training space is formed exclusively from calibrated out-of-fold probability representations. At the second stage, linear meta-models are used to aggregate probabilistic outputs and produce the final decision. The method was evaluated on a real-world dataset collected from the admission campaign of Lviv Polytechnic National University. Experimental studies using holdout validation and stratified cross-validation demonstrate that the proposed ensemble achieves high and stable predictive performance while preserving the quality of probabilistic estimates. In particular, the method reached F1-scores up to 0.990 and MCC values up to 0.979 on the holdout test set, together with low LogLoss values. Comparative analysis with baseline classifiers and standard stacking approaches confirms that calibrated probabilistic fusion improves both classification quality and the reliability of posterior probability estimates in practical decision-support tasks.