Ensemble Learning – Bagging and Boosting
In the previous chapter, we covered the fundamentals of machine learning (ML), working with data and models, and concepts such as overfitting and supervised learning (SL). We also introduced decision trees and saw how to apply them practically in scikit-learn.
In this chapter, we will learn about ensemble learning and the two most significant types of ensemble learning: bagging and boosting. We will cover the theory and practice of applying ensemble learning to decision trees and conclude the chapter by focusing on more advanced boosting methods.
By the end of this chapter, you will have a good understanding of ensemble learning and how to practically build decision tree ensembles through bagging or boosting. We will also be ready to dive deep into LightGBM, including its more advanced theoretical aspects.
The main topics we will cover are set out here:
- Ensemble learning
- Bagging and random forests
- Gradient-boosted...
