Chapter 4


Learning Objectives

By the end of this chapter, you will be able to:

  • Implement logistic regression and explain how it can be used to classify data into specific groups or classes
  • Use the K-nearest neighbors clustering algorithm for classification
  • Use decision trees for data classification, including the ID3 algorithm
  • Describe the concept of entropy within data
  • Explain how decision trees such as ID3 aim to reduce entropy
  • Use decision trees for data classification

This chapter introduces classification problems, classification using linear and logistic regression, K-nearest neighbors classification, and decision trees.


In the previous chapter, we began our supervised machine learning journey using ...

Get Applied Supervised Learning with Python now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.