Ensemble Machine Learning

Ensemble machine learning combines multiple individual machine learning models to improve predictive accuracy, robustness, and reliability beyond what any single model can achieve. Current research focuses on optimizing ensemble architectures, such as stacking and voting ensembles, and exploring their application across diverse fields, including healthcare (e.g., medication extraction and sepsis prediction), weather forecasting, and even music generation. This approach is proving valuable for tackling complex problems where high accuracy and confidence are crucial, leading to improved decision-making in various scientific and practical domains.