Regression Forest

Regression forests, an ensemble learning method extending random forests, aim to predict not only point estimates but also the entire probability distribution of a target variable, thereby providing crucial uncertainty quantification. Current research emphasizes quantile regression forests (QRFs) and their application in diverse fields, including financial forecasting, spatial interpolation of satellite data, and predictive process monitoring, often comparing them against other algorithms like gradient boosting machines and neural networks within stacking ensembles. This focus on probabilistic prediction and uncertainty estimation significantly enhances the reliability and interpretability of machine learning models, leading to more informed decision-making across various scientific and practical domains.