Ensemble Method

Ensemble methods combine multiple machine learning models to improve prediction accuracy and robustness, addressing limitations of individual models. Current research focuses on optimizing ensemble architectures, including neural networks, gradient boosting decision trees (like XGBoost, CatBoost, and LightGBM), and probabilistic regression trees, with emphasis on dynamic weighting of model contributions and efficient training strategies to reduce computational costs. These advancements are significantly impacting diverse fields, from medical diagnosis and multi-omics data analysis to anomaly detection and natural language processing, by providing more accurate and reliable predictions.