Random Forest vs Logistic Regression in Predictive Analytics Applications

Date

2018-11

Contributor

Advisor

Department

Instructor

Depositor

Speaker

Researcher

Consultant

Interviewer

Narrator

Transcriber

Annotator

Journal Title

Journal ISSN

Volume Title

Publisher

University of Hawaii - West Oahu

Volume

Number/Issue

Starting Page

Ending Page

Alternative Title

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.

Description

Keywords

Citation

Extent

1 page

Format

Geographic Location

Time Period

Related To

Related To (URI)

Table of Contents

Rights

Attribution-NonCommercial-NoDerivs 3.0 United States

Rights Holder

Local Contexts

Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.