In this chapter, we explore gradient boosting, a powerful ensemble machine learning
method, for both regression and classification tasks. With a focus on accessibility, we minimize
abstract mathematical theories and instead emphasize two concrete numerical examples with
small datasets related to predicting house sale prices and ease of selling houses in the property
market. By providing a step-by-step walkthrough, we illuminate the inner workings of gradient
boosting and offer sample codes and comparisons to the gradient boosting models available in
the scikit-learn library. Upon completing this chapter, readers will possess a comprehensive
understanding of gradient boosting's mechanics, its connection to the implementation and
performance of the algorithm, and be well-prepared to apply it in real-world projects.
Keywords: Gradient Boosting, Ensemble Learning, Regression, Classification, Numerical Example, Small Dataset, Scikit-Learn
