Applied Supervised Learning with Python

Chapter 4

Classification

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.

Introduction

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