# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 4 Results
### Experiments for MBA Dataset  
Using the PyCaret library, we trained and evaluated several machine learning classifiers, including Naive Bayes, AdaBoost, Decision Tree, Extra Trees, Random Forest, Extreme Gradient Boosting (XGBoost), Logistic Regression, Light Gradient Boosting Machine (LightGBM), and Gradient Boosting. To address the issue of class imbalance inherent in the dataset, we employed Stratified k-Fold Cross-Validation with k = 10, ensuring that each fold maintained the same class distribution as the full dataset. For performance evaluation, we used the metrics outlined in Section 3.4, ranking models in descending order based on the Matthews correlation coefficient (MCC). For fairness assessment, we employed metrics including selection rate, disparate impact, true positive rate (TPR), and false positive rate (FPR). In order to present the fairness metric results effectively, we opted to include only the worst-case scenarios for each combination of protected attributes (e.g., gender or race) in Table 2. Specifically, we reported the minimum values for metrics such as selection rate, disparate impact, and true positive rate (TPR), as well as the maximum value for the false positive rate (FPR). It is important to emphasize that the worst-case values for these fairness metrics do not necessarily occur within the same combinations of protected attributes but rather across different ones. In analyzing classifier performance on the MBA Dataset, distinct strengths emerge for both the Naive Bayes and Logistic Regression models, reflecting their suitability for different classification objectives. The Naive Bayes model demonstrates a notable advantage \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Performance Measures**} & \multicolumn{6}{c}{**Fairness Measures**} \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Prec.**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**Kappa**} & \multirow{2}{*}{**MCC**} & \multirow{2}{*}{\begin{tabular}{c} **Selection** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Type** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **False** \\ **Positive** \\ **Rate (TPR)** \\ \end{tabular} } \\ \hline Ada Boost Classifier & 0.6964 & 0.6290 & 0.3250 & **0.5965** & 0.4037 & **0.2222** & **0.2506** & 0.045455 & 0.25756 & 0 & 0.5 \\ Ridge Classifier & 0.6929 & 0.6522 & 0.2250 & 0.5452 & 0.3087 & 0.1610 & 0.1893 & 0.045455 & 0.257576 & 0 & 0.214268 \\ Logistic Regression & **0.6893** & **0.6528** & **0.2139** & 0.6000 & **0.3016** & 0.1525 & 0.1947 & 0.045455 & 0.193182 & 0 & 0.214286 \\ Linear Discriminant Analysis & 0.6893 & 0.6533 & 0.2261 & 0.5467 & 0.3180 & 0.1624 & 0.1906 & 0.045455 & 0.193182 & 0 & 0.214286 \\ Naive Bayes & 0.6821 & 0.6590 & 0.3917 & 0.5060 & 0.4368 & 0.2221 & 0.2279 & 0.045455 & 0.154545 & 0.111111 & 0.571429 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance and fairness metrics for ML models for College Admission Dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Performance Measures**} & \multicolumn{6}{c}{**Fairness Measures**} \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Prec. **} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**Kappa**} & \multirow{2}{*}{**MCC**} & \multirow{2}{*}{**Selection**} & \multirow{2}{*}{**Disparate**} & \multirow{2}{*}{**TPR**} & \multirow{2}{*}{**FPR**} \\ \cline{1-1} \clinein metrics such as precision (0.8862), Kappa (0.9057), and Matthews correlation coefficient (MCC, 0.2719). These metrics highlight the model's strengths in scenarios requiring precision and reliable differentiation between classes.