Random Forest

Random forests are ensemble learning methods that combine multiple decision trees to improve predictive accuracy and robustness. Current research focuses on enhancing their performance through techniques like optimizing bootstrap sampling rates, improving feature selection methods (e.g., using integrated path stability selection), and developing efficient machine unlearning frameworks to address privacy concerns. These advancements are impacting diverse fields, from medical diagnosis and finance to materials science and environmental monitoring, by providing accurate and interpretable predictive models for complex datasets.