XGBoost Model
XGBoost, an extreme gradient boosting algorithm, is a powerful machine learning technique primarily used for classification and regression tasks, aiming to achieve high predictive accuracy and efficiency. Current research focuses on optimizing XGBoost for various applications, including its integration into more complex models like diffusion and flow-based systems, and its use in ensemble methods with other algorithms such as LightGBM, neural networks, and even incorporating fuzzy logic or attention mechanisms. The algorithm's versatility and strong performance have led to its widespread adoption across diverse fields, from financial modeling and medical diagnosis to agricultural applications and even sports analytics, significantly impacting both scientific understanding and practical decision-making.
Papers
The effect of different feature selection methods on models created with XGBoost
Jorge Neyra, Vishal B. Siramshetty, Huthaifa I. Ashqar
Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations
Pablo Gómez, Roland D. Vavrek, Guillermo Buenadicha, John Hoar, Sandor Kruk, Jan Reerink