While this chapter was rather long, you have entered the world of tree based algorithms, and left with a wide arsenal of tools that you can implement in order to solve both small- and large-scale problems. To summarize, you have learned the following:
- How to use decision trees for classification and regression
- How to use random forests for classification and regression
- How to use AdaBoost for classification
- How to use gradient boosted trees for regression
- How the voting classifier can be used to build a single model out of different models
In the upcoming chapter, you will learn how you can work with data that does not have a target variable or labels, and how to perform unsupervised machine learning in order to solve such problems!
