XgBoost is an efficient and scalable machine learning algorithm that combines weak models to produce a stronger prediction. It is widely used for classification and regression tasks, especially with large datasets. XgBoost offers high performance, scalability, customizability, and handles missing values. It has advantages such as producing high-quality results and feature importances, but it can be computationally complex, prone to overfitting, and requires careful hyperparameter tuning.
