chapter eight
This chapter covers:
- What is linear regression?
- What performance metrics we use for regression tasks
- How to use machine learning algorithms to impute missing values
- How to perform feature selection algorithmically
- How to combine preprocessing wrappers in mlr
- What are generalized additive models (GAMs)?
Our first stop in regression brings us to linear regression and generalized additive models. Both of these techniques rely on the equation of a straight line to build models that predict a continuous variable. Each approach allows us to combine categorical and continuous predictor variables, but they differ in how complex they allow the relationship between the predictor and outcome variables to be. While linear regression models the relationship between each predictor variable and the outcome as a straight line, generalized additive models are more flexible (literally) as they allow for complex, non-linear relationships between predictors and outcome.