# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 4 Results
### Experiments for College Admission Dataset  
Using the procedure outlined earlier, classifiers were trained and evaluated using the PyCaret library. The five top-performing models, based on the evaluation metrics, were Naive Bayes, AdaBoost, Logistic Regression, Ridge Regression, and Linear Discriminant Analysis. These classifiers demonstrated superior performance relative to others in the context of this dataset.  
The results, summarized in Table 4, were ranked based on accuracy rather than MCC. This adjustment in ranking criteria reflects the balanced nature of the dataset, as the absence of significant imbalances diminishes the relevance of MCC as a primary performance metric. Accuracy is more indicative of classifier performance in balanced datasets and it also provides a more meaningful basis for classifier comparison.  
For the fairness analysis, we considered only gender and race as protected attributes, deliberately excluding socioeconomic status from the evaluation.  
The AdaBoost classifier achieved the highest accuracy (0.6964), marginally outperforming both the Ridge Classifier (0.6929) and Logistic Regression (0.6893). Additionally, AdaBoost exhibited the highest Matthews correlation coefficient (MCC) of 0.2506 and a Kappa value of 0.2222, closely followed by Naive Bayes in both metrics. Regarding the Area Under the Curve (AUC), [PERSON] led with a score of 0.6590, surpassing both Logistic Regression (0.6528) and Linear Discriminant Analysis (LDA) (0.6533).  
Naive Bayes also excelled in recall (0.3917) and F1-score (0.4368), with AdaBoost ranking second in these metrics (recall: 0.3250; F1-score: 0.4037). Interestingly, Logistic Regression achieved the highest precision (0.6000), narrowly beating AdaBoost. These results indicate that the top-performing classifiers were AdaBoost, Logistic Regression (and its variants), and Naive Bayes, showcasing complementary strengths across different performance metrics.  
Beyond predictive performance, fairness-related metrics revealed nuanced insights. All models demonstrated identical worst-case selection rates of 0.0455. AdaBoost and the  
Figure 2: Feature importance of Logistic Regression for Law School Dataset.  
Ridge Classifier shared the highest worst-case disparate impact (0.2576), although this value remains well below the typical fairness threshold of 0.8, suggesting potential challenges in equitable decision-making. Notably, Naive Bayes was the only model with a non-zero worst-case true positive rate (TPR) of 0.1111, indicating a relatively higher proportion of equitable positive predictions. However, this came at the cost of an increased false positive rate (FPR), highlighting a trade-off between fairness and precision.  
Despite these differences, the overall performance metrics suggest that the classifiers do not significantly differ in terms of accuracy or fairness. Each model exhibits specific strengths and weaknesses, making their suitability context-dependent. For example, while AdaBoost delivers robust accuracy and balanced performance, Naive Bayes stands out for recall and fairness-related metrics. Logistic Regression offers the highest precision, ideal for applications prioritizing correct positive predictions.  
If we were to select one of these classifiers, we would likely choose Naive Bayes as the most balanced option in terms of both performance and fairness metrics.