Prediction Model of Pancreatic Neoplasms in CP Patients With Focal Pancreatic Lesions

XGBoost is a powerful machine learning algorithm known for its efficiency and performance. It is an optimized gradient boosting library designed to be highly efficient, flexible, and portable. XGBoost works by combining multiple weak prediction models, typically decision trees, to produce a strong predictive model. It supports various objective functions and evaluation metrics, making it suitable for a wide range of tasks, including classification and regression. XGBoost also includes features like regularization to prevent overfitting and can handle missing data effectively.