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PERC 2022 Abstract Detail Page

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Abstract Title: Using random forests to study physics graduate school admissions
Abstract Type: Symposium Talk
Abstract: Random Forest is a machine learning technique designed for both creating predictive models and determining which variables in a dataset are most useful for making those predictions. In this talk, I will describe how the random forest algorithm works and when it might be preferable over more traditional quantitative methods. I will also present an example of how the algorithm works in practice by determining what parts of a physics graduate school application are predictive of admission.
Session Time: Parallel Sessions Cluster III
Room: Vandenberg A
Parallel Session: Machine learning methods in PER: Intuition and methodological discussion

Author/Organizer Information

Primary Contact: Nicholas Young
University of Michigan