Machine Learning with R, the tidyverse, and mlr

Chapter 13. Maximizing variance with principal component analysis

This chapter covers

• Understanding dimension reduction
• Dealing with high dimensionality and collinearity
• Using principal component analysis to reduce dimensionality

Dimension reduction comprises a number of approaches that turn a set of (potentially many) variables into a smaller number of variables that retain as much of the original, multidimensional information as possible. We sometimes want to reduce the number of dimensions we’re working with in a dataset, to help us visualize the relationships in the data or to avoid the strange phenomena that occur in high dimensions. So dimension reduction is a critical skill to add to your machine learning toolbox!

Our first stop in ...