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<h2>Random Forests: Classification & Regression Trees</h2> <blockquote> <p>RSVP for all sessions at: <a href="http://statstudio_cehs_usu.eventbrite.com" rel="nofollow">Stat Studio on Eventbrite</a></p> </blockquote> <ul> <li>2017, September 22nd 10:00-noon, EDUC 454 by Sarah Schwartz</li> <li>2017, March 17th 1:00-3:00pm, EDUC 454, by Sarah Schwartz</li> </ul> <p>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. </p> <p>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. </p> <p>This workshop will demonstrate the use of <strong>Random Forests</strong> 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.</p>
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