Random Forest Classifier

Random Forest classifiers are ensemble learning methods used for both classification and regression tasks, aiming to improve predictive accuracy and robustness compared to individual decision trees. Current research focuses on enhancing Random Forest performance through techniques like open-set recognition to handle unknown classes, integrating it with other models for improved interpretability, and optimizing feature selection for specific applications. These advancements are impacting diverse fields, from healthcare diagnostics and cybersecurity (e.g., ransomware detection) to image analysis and environmental monitoring, by providing accurate and, increasingly, explainable classification models.