Tree based algorithms are very popular for two reasons: they are interpretable, and they make sound predictions that have won many machine learning competitions on online platforms, such as Kaggle. Furthermore, they have many use cases outside of machine learning for solving problems, both simple and complex.
Building a tree is an approach to decision-making used in almost all industries. Trees can be used to solve both classification- and regression-based problems, and have several use cases that make them the go-to solution!
This chapter is broadly divided into the following two sections:
- Classification trees
- Regression trees
Each section will cover the fundamental theory of different types of tree based algorithms, along with their implementation in scikit-learn. By the end of this chapter, you will have learned how to aggregate several...
