Numerical Machine Learning

Gradient Boosting

Author(s): Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar *

Pp: 116-159 (44)

DOI: 10.2174/9789815136982123010007

* (Excluding Mailing and Handling)


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

Related Journals
Related Books
© 2024 Bentham Science Publishers | Privacy Policy