Random Forest Model

Random Forest models are ensemble learning methods that combine multiple decision trees to improve predictive accuracy and robustness. Current research emphasizes enhancing their interpretability, addressing bias in training data, and optimizing their performance in high-dimensional or low-sample-size scenarios, often through techniques like alpha-trimming or novel kernel methods. These models find broad application across diverse fields, from healthcare (sepsis prediction, chronic disease prognosis) and environmental science (air pollution assessment, debris flow prediction) to transportation and finance, demonstrating their versatility and impact on real-world problems. The ongoing focus is on improving both predictive power and the explainability of these powerful "black box" models.