Predictive Analytics with Ensemble Learning
In this chapter, we will learn about ensemble learning and how to use it for predictive analytics. By the end of this chapter, you will have a better understanding of these topics:
- Decision trees and decision trees classifiers
- Learning models with ensemble learning
- Random forests and extremely random forests
- Confidence measure estimation of predictions
- Dealing with class imbalance
- Finding optimal training parameters using grid search
- Computing relative feature importance
- Traffic prediction using the extremely random forests regressor
Let's begin with decision trees. Firstly, what are they?
