Prediction Model

Prediction models aim to accurately forecast outcomes based on input data, with current research focusing on improving model robustness, interpretability, and fairness. Commonly employed architectures include linear regression, decision trees, support vector machines, neural networks (including deep learning models and transformers), and ensemble methods like XGBoost, often enhanced by techniques like data preprocessing, feature engineering, and counterfactual explanations. These advancements are crucial for diverse applications, ranging from healthcare and finance to environmental monitoring and autonomous systems, improving decision-making and resource allocation across various sectors.