Applied Supervised Learning with Python

Chapter 3

Regression Analysis

Learning Objectives

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

• Describe regression models and explain the difference between regression and classification problems
• Explain the concept of gradient descent, how it is used in linear regression problems, and how it can be applied to other model architectures
• Use linear regression to construct a linear model for data in an x-y plane
• Evaluate the performance of linear models and use the evaluation to choose the best model
• Use feature engineering to create dummy variables for constructing more complicated linear models
• Construct time series regression models using autoregression

This chapter covers regression problems and analysis, introducing us to linear ...