This chapter focuses on XGBoost (Extreme Gradient Boosting), building directly on the foundational gradient boosting concepts and regularization techniques discussed earlier. XGBoost is a popular and effective gradient boosting implementation, distinguished by several key improvements designed for better performance and accuracy.

We will examine the core components that make XGBoost effective:

• Regularized Learning Objective: Understanding how XGBoost incorporates $L_1$ and $L_2$ penalties directly into the objective function to control model complexity.
• Split Finding Algorithms: Studying the exact greedy algorithm for finding splits and the approximate algorithms used for larger datasets.
• Handling Sparsity: Learning about the built-in mechanisms for efficiently managing missing values.
• System Optimizations: Reviewing the techniques like parallel processing and cache optimization that enhance training speed.

By the end of this chapter, you will understand the technical details behind XGBoost and be prepared to implement it using its Python library, configuring its main parameters for practical tasks.