Random Forest vs Logistic Regression in Predictive Analytics Applications
| dc.contributor.author | Palacat, Christi | |
| dc.contributor.instructor | Wen, Eric | |
| dc.date.accessioned | 2019-05-25T00:29:44Z | |
| dc.date.available | 2019-05-25T00:29:44Z | |
| dc.date.issued | 2018-11 | |
| dc.description.abstract | Are there significant differences in prediction accuracy between modern data-mining approaches and classical regression methods? Which approach is easier to explain to enrollment managers when estimating student outcomes of varying levels of complexity? Complexity in the data is typically associated with quality, quantity, and the interaction of predictor variables. To test for such differences, this study compares the classification accuracy of a random forest algorithm with binomial logistic regression for purposes of predicting student admission yield. Findings are translated into operationally meaningful indicators in the context of enhanced institutional research on yield prediction and enrollment forecast analysis. Although the selection of predictor variables is guided by the research on estimating admission yield at a large public university, the presentation focuses on which method promises greater prediction accuracy and how easily each approach can be explained to enrollment managers who also desire interpretable results. | |
| dc.format.extent | 1 page | |
| dc.identifier.uri | http://hdl.handle.net/10790/4636 | |
| dc.language.iso | en-US | |
| dc.publisher | University of Hawaii - West Oahu | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.title | Random Forest vs Logistic Regression in Predictive Analytics Applications | |
| dc.type | Presentation | |
| dc.type.dcmi | Text |