Machine Learning with R, the tidyverse, and mlr

Chapter 9. Linear regression

This chapter covers

• Working with linear regression
• Performance metrics for regression tasks
• Using machine learning algorithms to impute missing values
• Performing feature selection algorithmically
• Combining preprocessing wrappers in mlr

Our first stop in part 3, “Regression,” brings us to linear regression. A classical and commonly used statistical method, linear regression builds predictive models by estimating the strength of the relationship between our predictor variables and our outcome variable. Linear regression is so named because it assumes the relationships between the predictor variables with the outcome variable are linear. Linear regression can handle both continuous and categorical predictor variables, ...