10. QUA-PRE-010 — Predictive Modelling, Machine Learning, Model Validation and Robustness Analysis

Description:

This Master Prompt would focus on moving quantitative analysis from conventional statistical explanation toward prediction, model comparison, uncertainty assessment and robustness evaluation. It would guide researchers through training-validation-test strategies, cross-validation, feature selection, regression and classification algorithms, hyperparameter optimization, model benchmarking and appropriate predictive-performance measures such as R², RMSE, MAE, accuracy, sensitivity, specificity, precision, recall, F1-score and ROC-AUC.

Where appropriate, it would extend into Random Forest, XGBoost, neural networks and deep-learning approaches while maintaining the distinction between statistical explanation and predictive performance. Researchers would also be guided to investigate overfitting, generalizability, uncertainty and robustness.

This theme is strongly represented in the uploaded abstracts. One study combines XGBoost feature selection with an optimized MLP for classification and quantitative regression, while another uses Bayesian CNNs and active learning to combine prediction with uncertainty estimation and sample efficiency.