Random Forests: Classification & Regression Trees ------------------------------------------------- > RSVP for all sessions at: [Stat Studio on Eventbrite][1] - 2017, September 22nd 10:00-noon, EDUC 454 by Sarah Schwartz - 2017, March 17th 1:00-3:00pm, EDUC 454, by Sarah Schwartz It is common in statistical modeling to have far more predictor variables available than will be included in a final model (especially when including combinations of variables as interaction effects). Often, predictor variables are correlated with one another and have differing patterns of missingness. In order to arrive at the most parsimonious statistical model that accounts for as much of the variance in the outcome as possible, a systematic approach to variable selection is needed, which also allows for interactions, correlations, missingness, and unbalancedness across groups. This workshop will demonstrate the use of **Random Forests** as a method to arrive at the strongest and most parsimonious set of predictor variables. Although a limitation of Random Forests is interpretability of effects, methods for using the results of Random Forests as a guide in other modeling approaches will be presented. [1]: http://statstudio_cehs_usu.eventbrite.com