Random Forest Prediction

Random forest prediction is a machine learning technique used for both classification and regression tasks, aiming to improve predictive accuracy and interpretability. Current research focuses on enhancing random forest performance in scenarios with limited data, developing quantum-based implementations for faster computation, and improving explainability through methods that highlight the most influential training examples or feature attributions. These advancements are significant for various scientific fields and practical applications, enabling more efficient data analysis, more reliable predictions, and better understanding of complex models.