Eco 6380 Predictive Analytics For Economists Spring 2014 Professor Tom Fomby Department of Economics SMU

Presentation 18 Principal Components Analysis Chapter 4 in SPB

Principal Components as a Dimension Reduction Technique In PPT3 we talked about Variable Importance Measures and how they could be used to reduce the number of variables under consideration for predictive analytics problems for the purpose of building more parsimonious models that produce more accurate prediction or classification results. We also discussed the use of subset selection methods in MLR, the building of parsimonious trees, and the use of LASSO regression for picking important explanatory variables for building more accurate prediction and classification models. The Principal Components method is another method by which one can build a parsimonious input space for determining more accurate predictors and classifiers. The Principal Components method creates orthogonal indices (called Principal Components) of the original data and then chooses a subset of them as explanatory variables (inputs) for prediction or classification. For a full description of the Principal Components approach to input space reduction see the pdf file ”Math Notation for PCA.pdf.”

Classroom Exercise: Exercise 14