Standard Random Forest

Standard Random Forests are ensemble learning methods that combine multiple decision trees to improve predictive accuracy and robustness. Current research focuses on enhancing Random Forest performance through techniques like adaptive tree pruning (e.g., alpha-trimming), incorporating functional data representations (e.g., randomized spline trees), and optimizing the underlying piecewise linear models (e.g., JOPLEn). These advancements aim to improve prediction accuracy across diverse applications, from environmental time series analysis to personalized medicine and wildfire prediction, by addressing limitations in handling complex data structures and improving generalization capabilities.