Practical Machine Learning with R

Chapter 5

Linear and Logistic Regression Models

Learning Objectives

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

• Implement and interpret linear and logistic regression models
• Compare linear and logistic regression models with cvms
• Implement a random forest model
• Create baseline evaluations with cvms
• Select nondominated models, when metrics rank models differently

In Chapter 1, An Introduction to Machine Learning, we were introduced to linear and logistic regression models. In this chapter, we will expand our knowledge of these tools and use cross-validation to compare and choose between a set of models.

Introduction

While neural networks are often better than linear and logistic regression models at solving regression and classification ...