When building a model, we want to keep only the relevant features and discard the nonrelevant ones. A model that uses redundant features will have a higher variance (that it, it will be less precise) and the predictions will not look that great. When building models, keeping redundant features is comparable to omitting the relevant ones.
Variable importance and feature selection
Getting ready
The caret package needs to be installed using install.packages("caret").
How to do it...
In this recipe, we will focus on removing irrelevant features from our models,...
