Applied Supervised Learning with R

Chapter 3:

Introduction to Supervised Learning

Learning Objectives

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

• Explain supervised learning and machine learning workflow
• Use and explore the Beijing PM2.5 dataset
• Explain the difference between continuous and categorical dependent variables
• Implement the basic regression and classification algorithms in R
• Identify the key differences between supervised learning and other types of machine learning
• Work with the evaluation metrics of supervised learning algorithms
• Perform model diagnostics for avoiding biased coefficient estimates and large standard errors

In this chapter, we will introduce supervised learning and demonstrate the workflow of building machine learning models with real-world ...