Regression Tree

Regression trees are a machine learning method aiming to predict a continuous outcome by recursively partitioning the feature space into regions, each associated with a simple prediction model (often a constant or linear function). Current research emphasizes improving prediction accuracy through techniques like adaptive pruning (e.g., alpha-trimming), ensemble methods (random forests, gradient boosting), and incorporating probabilistic predictions. These advancements enhance the applicability of regression trees across diverse fields, from time series forecasting and anomaly detection to handling missing data and improving the interpretability of complex models.