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Developing interpretable machine learning models has become an increasinglyimportant issue. One way in which data scientists have been able to developinterpretable models has been to use dimension reduction techniques. In thispaper, we examine several dimension reduction techniques including two recentapproaches developed in the network psychometrics literature called exploratorygraph analysis (EGA) and unique variable analysis (UVA). We compared EGA andUVA with two other dimension reduction techniques common in the machinelearning literature (principal component analysis and independent componentanalysis) as well as no reduction to the variables real data. We show that EGAand UVA perform as well as the other reduction techniques or no reduction.Consistent with previous literature, we show that dimension reduction candecrease, increase, or provide the same accuracy as no reduction of variables.Our tentative results find that dimension reduction tends to lead to betterperformance when used for classification tasks.

An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics