Chapter 3. Predictive Analytics with Ensemble Learning
In this chapter, we are going to learn about Ensemble Learning and how to use it for predictive analytics. By the end of this chapter, you will know these topics:
- Building learning models with Ensemble Learning
- What are Decision Trees and how to build a Decision Trees classifier
- What are Random Forests and Extremely Random Forests, and how to build classifiers based on them
- Estimating the confidence measure of the predictions
- Dealing with class imbalance
- Finding optimal training parameters using grid search
- Computing relative feature importance
- Predicting traffic using Extremely Random Forests regressor
