How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Researchers have developed a new method for optimizing the number of trees in Random Forest models, addressing a common challenge in hyperparameter tuning. Their approach uses a triplet-based plateau-search algorithm that adaptively identifies a near-minimal sufficient ensemble size by monitoring changes in the out-of-bag score. This method aims to provide a more automated and interpretable procedure compared to traditional techniques, with experiments suggesting it can select fewer trees than common heuristics on benchmark datasets but more on certain high-dimensional bioinformatics datasets. AI
IMPACT Introduces a novel optimization technique for ensemble models, potentially improving efficiency and performance on specific datasets.