PERC 2022 Abstract Detail Page
Previous Page | New Search | Browse All
| 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 |
