TL;DR
A Bayesian optimization super learner was developed that takes the probability outputs of base learners as input features and integrates them through the super learner weighted ensemble algorithm, thereby quantifying the reliability and uncertainty of predictions.
Logistic Regression, Random Forest, soft voting, and weighted voting using a publicly available educational dataset comprising 4,424 student records, 36 predictors, and three outcome classes provided accurate, interpretable, and decision-oriented predictions, although external validation and prospective evaluation are...
Overall, the findings demonstrate that integrating feature selection with model optimization substantially enhances classification performance, with the optimized Random Forest emerging as the most accurate and balanced model for the evaluated dataset.
A Standardized Multi-Metric Evaluation Framework is developed to support the selection of more objective and reproducible machine learning models in the case of loan approval to identify models that have a balance of performance and efficiency in loan approval experiments.
Empirical findings conclusively demonstrate that ensemble and deep learning methods significantly surpass traditional statistical approaches in predictive accuracy and robustness in Computer-Aided Learning systems, and provide strong empirical support for integrating advanced ML models into CAL environments.
The experimental results demonstrated that the proposed stacking model achieves strong predictive performance, with an accuracy of 95.82% and an Area Under the ROC Curve (AUC) close to 1.0 on the primary dataset, while maintaining strong generalization across external datasets.
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