Ensemble Regression

Ensemble regression combines multiple regression models to improve predictive accuracy and robustness compared to individual models. Current research emphasizes enhancing ensemble methods through optimized aggregation techniques, particularly for distributed computing and handling noisy data, as well as incorporating uncertainty quantification for improved reliability and explainability. These advancements are driving applications across diverse fields, including healthcare (predicting medical costs and RV volume), robotics (SLAM error prediction), and network management (anomalous traffic prediction), by providing more accurate, reliable, and interpretable predictions.