Abstract
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.
Related Journals
Recent Patents on Computer Science
The Chinese Journal of Artificial Intelligence
Current Chinese Computer Science
Journal of Fuzzy Logic and Modeling in Engineering
Recent Advances in Computer Science and Communications
International Journal of Sensors, Wireless Communications and Control
Journal of Intelligent Systems in Current Computer Engineering
Current E-Learning
Current Computer Science
Wireless and Communication Letters
Related Books
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
A First Course in Artificial Intelligence
Artificial Intelligence: Models, Algorithms and Applications
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
Handbook of Mobile Application Development: A Guide to Selecting the Right Engineering and Quality Features
Applications of Modern High Performance Networks
Arduino Meets Matlab: Interfacing, Programs and Simulink
Arduino and SCILAB based Projects
Application of Chaos and Fractals to Computer Vision