You have constructed gradient boosting models using Scikit-Learn, XGBoost, and other advanced libraries. While the default settings of these libraries provide a reasonable baseline, achieving high performance for a specific problem requires a methodical process of adjustment and optimization. This process is known as hyperparameter tuning.

This chapter provides a structured guide to optimizing your gradient boosting models. We will begin by identifying the most influential hyperparameters that control model behavior, such as the number of boosting stages ($M$), the learning rate ($\eta$), and parameters that regulate the complexity of individual decision trees.

You will learn how these settings affect the bias-variance tradeoff and how to manipulate them to prevent overfitting. We will cover techniques for regularization, including row and column subsampling. Finally, we will implement systematic search strategies, including Grid Search and Randomized Search, to automate the process of finding effective hyperparameter combinations for your dataset. The chapter concludes with a practical exercise where you will apply these tuning techniques to improve a model's predictive accuracy.