USU - Stat Studio: Workshop Series  /

USU - Stat Studio: Workshop - Random Forests

Date created: | Last Updated:


Creating DOI. Please wait...

Create DOI

Category: Methods and Measures

Description: 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.

License: CC0 1.0 Universal


Random Forests: Classification & Regression Trees RSVP for all sessions at: Stat Studio on Eventbrite 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...


Loading files...



Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.