<ul><li>Accurate and reproducible wine-quality assessment is essential for production control.</li><li>A unified benchmark of five ensemble learners (Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost) was conducted on Vinho Verde red- and white-wine datasets.</li><li>Gradient Boosting showed the highest accuracy in the study, followed closely by Random Forest and XGBoost.</li><li>The study recommended Random Forest as the most cost-effective model for wine-quality prediction.</li></ul>

Wine Quality Prediction with Ensemble Trees: A Unified, Leak-Free Comparative Study

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