What is XGBoost?

XGBoost (Extreme Gradient Boosting) is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solves many data science problems in a fast and accurate way.

Advantages of XGBoost

Performance:

XGBoost is known for its high performance and speed. It leverages multiple cores for parallel processing and is optimized for both memory and computation.

Regularization: XGBoost has built-in L1 (Lasso) and L2 (Ridge) regularization to prevent overfitting.

Handling Missing Values: XGBoost can handle missing values internally, making it robust and flexible.

Flexibility: XGBoost supports various objective functions, including regression, classification, and ranking.

Scalability: It can handle large datasets efficiently and can be used in distributed and parallel computing environments.