Chapter 5. Decision Tree based learning
Starting this chapter, we will take a deep dive into each of the Machine learning algorithms. We begin with a non-parametric supervised learning method, Decision trees, and advanced techniques, used for classification and regression. We will outline a business problem that can be addressed by building a Decision tree-based model and learn how it can be implemented in Apache Mahout, R, Julia, Apache Spark, and Python.
The following topics are covered in depth in this chapter:
- Decision trees: definition, terminology, the need, advantages, and limitations.
- The basics of constructing and understanding Decision trees and some key aspects such as Information gain and Entropy. You will also learn to build regression, the classification of trees and measuring errors.
- Understanding some common problems with Decision trees, need for pruning Decision trees, and techniques for pruning.
- You will learn Decision tree algorithms such as CART, C4.5, C5.0 and...
