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Practical Machine Learning by Sunila Gollapudi

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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, ...

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