Super Learner

Super Learner is an ensemble machine learning technique that combines predictions from multiple base models to improve overall accuracy and robustness. Current research focuses on extending its application to diverse areas, including causal inference, spatial modeling (e.g., for wildfire prediction and air pollution mapping), and robust estimation in the presence of outliers or heterogeneous data, often employing variations of loss functions like Huber loss or novel aggregation methods such as superquantile aggregation. This approach offers significant advantages over single-model approaches by providing more accurate and reliable predictions across various domains, leading to improved decision-making in fields ranging from healthcare to environmental science.