With a theoretical understanding of how the Gradient Boosting Machine (GBM) works, we now shift to its practical application. This chapter concentrates on implementing these models using Scikit-Learn, a standard library in the Python machine learning stack. Its consistent API provides a direct path for building your first gradient boosting models.

You will work with Scikit-Learn's two primary implementations: GradientBoostingClassifier for classification tasks and GradientBoostingRegressor for regression problems. We will cover the standard process of fitting these models to data, generating predictions, and understanding the main parameters that govern their behavior, such as the number of boosting stages ($n\_estimators$) and the learning rate.

Building a model is only part of the process; interpretation is just as important. We will also address how to make sense of a trained model. You will learn to extract feature importance scores to identify which variables most influence predictions and use partial dependence plots to visualize the relationship between individual features and the model's output.