  Developing interpretable machine learning models has become an increasingly
important issue. One way in which data scientists have been able to develop
interpretable models has been to use dimension reduction techniques. In this
paper, we examine several dimension reduction techniques including two recent
approaches developed in the network psychometrics literature called exploratory
graph analysis (EGA) and unique variable analysis (UVA). We compared EGA and
UVA with two other dimension reduction techniques common in the machine
learning literature (principal component analysis and independent component
analysis) as well as no reduction to the variables real data. We show that EGA
and UVA perform as well as the other reduction techniques or no reduction.
Consistent with previous literature, we show that dimension reduction can
decrease, increase, or provide the same accuracy as no reduction of variables.
Our tentative results find that dimension reduction tends to lead to better
performance when used for classification tasks.
