29: Interpretable Statistical and Machine Learning Models for Understanding MBA Admissions Decisions

Thomas M. Menino Convention & Exhibition Center 

Admissions decisions in graduate programs involve considering academic excellence and professional readiness across large and heterogeneous applicant pools. While logistic regression is a standard analytical approach, it may fail to capture nonlinear relationships and interaction effects. This study applies a multi-method framework to examine MBA admissions for 6,000 applicants. The analysis combines logistic regression, Random Forests, Gradient Boosting Decision Trees with SHAP value plots, and Chi-square Automatic Interaction Detection segmentation. Logistic regression offers interpretable odds ratios and statistical inference, while tree-based models capture nonlinearities. SHAP values are used to explain individual predictions, and CHAID identifies distinct subgroups.

Across all methods, GMAT score emerges as the dominant predictor, with GPA playing secondary role. Demographic variables exhibit detectable effects. Notably, results are highly concordant across models. CHAID segmentation reveals segment structures. This work demonstrates how integrating classical models with explainable machine learning enhances transparency and is broadly applicable to selection processes.

Logistic regression

GBDT

CHAID segmentation 

Section on Statistical Learning and Data Science