In our previous note, we discussed some problems that can arise when using standard principal components analysis (specifically, principal components regression) to model the relationship between independent (*x*) and dependent (*y*) variables. In this note, we present some dimensionality reduction techniques that alleviate some of those problems, in particular what we call *Y-Aware Principal Components Analysis*, or *Y-Aware PCA*. We will use our variable treatment package `vtreat`

in the examples we show in this note, but you can easily implement the approach independently of `vtreat`

.

Continue reading Principal Components Regression, Pt. 2: Y-Aware Methods