XGBoost (eXtreme Gradient Boosting) is an advanced machine learning algorithm based on the gradient boosting framework, which builds models sequentially by combining several weak learners (typically decision trees) to create a more robust and accurate model. It operates by iteratively adding trees that correct the errors of previous models.

XGBoost has several advantages over traditional gradient boosting algorithms, mainly due to its scalability, regularization capabilities, and optimization techniques.

XGBoost incorporates several optimizations and enhancements, making it faster and more efficient than standard gradient boosting models:

• Tree-based Ensemble Method: Like other boosting algorithms, XGBoost works by combining several weak learners (decision trees) to form a strong predictive model.
• Gradient Boosting Algorithm: XGBoost uses gradient boosting to minimize errors by fitting each subsequent tree to the residuals (errors) of the previous trees. The model updates itself iteratively to reduce the overall prediction error.
• Regularization: XGBoost includes both L1 (Lasso) and L2 (Ridge) regularization techniques to prevent overfitting. Regularization terms in the objective function control the complexity of the model, which helps generalize better to new data.
• Handling Missing Data: XGBoost can automatically handle missing values in the dataset. It assigns the missing values a direction in the decision tree that minimizes the loss, making it robust to incomplete data.
• Parallel and Distributed Computing: XGBoost is optimized for parallelization, enabling it to leverage modern hardware to accelerate training. It also supports distributed computing, allowing it to scale across clusters for handling very large datasets.
• Out-of-Core Computation: XGBoost can perform computations that don’t fit into memory, making it capable of working with very large datasets.
• Support for Multiple Data Types: XGBoost works well with both continuous and categorical data, making it versatile for many types of machine learning tasks.