# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 4 Results
### Experiments for Law School Dataset  
Following exactly the same procedure as described earlier for the MBA Dataset, classifiers including Naive Bayes, AdaBoost, Logistic Regression, Gradient Boosting, CatBoost, Extreme Gradient Boosting, Random Forest, Light Gradient Boosting, and Extra Trees were trained and evaluated using the PyCaret library in Table 3.  
In the Law School Dataset, the Naive Bayes model outperforms other classifiers on specific metrics, including precision (0.9541), Kappa (0.3474), and Matthews correlation coefficient (MCC, 0.3706). These results indicate that Naive Bayes excels at minimizing false positives while maintaining a moderate positive correlation between predicted and actual classifications. This performance makes it particularly effective in scenarios where precision is paramount, such as identifying true positives while avoiding over-classifying instances as positive incorrectly.  
Conversely, Logistic Regression demonstrates consistently outstanding performance across a broader range of metrics, positioning itself as the most well-rounded classifier. It achieves high scores in accuracy (0.9097), Area Under the Curve (AUC, 0.8722), recall (0.9840), and F1-score (0.9516). These metrics highlight its capability to balance precision and recall effectively, ensuring high sensitivity to true positives while maintaining robust overall classification performance. This balance is especially critical in scenarios where the cost of false negatives is high, such as legal or educational contexts where missing a key outcome could have significant implications. Moreover, the model's simplicity, interpretability, and computational efficiency further enhance its appeal, making it an ideal choice for applications requiring both reliable performance and transparent decision-making.  
Figure 2 visualizes the feature importance derived from the Logistic Regression model applied to the Law School Dataset. This plot reveals the relative contributions of each feature to the model's predictions, offering valuable insights into the primary factors influencing outcomes. Such analysis not only enhances our understanding of the dataset's underlying patterns but also informs strategic decision-making by identifying critical variables. These insights can be leveraged to refine data collection strategies, improve feature engineering, or guide interventions aimed at addressing key influences within the dataset.  
Further efforts to enhance class balance using Synthetic Minority Oversampling Technique (SMOTE) and attempts to combine the strengths of top-performing models through ensemble methods (hard or soft voting) did not result in any measurable improvements in either performance or fairness metrics. This outcome suggests that the underlying structure of the dataset and the distribution of features may inherently limit the potential for gains through oversampling or ensemble strategies.  
These findings emphasize the importance of tailoring model selection and optimization strategies to the specific characteristics of the dataset. While Naive Bayes and Logistic Regression each excel in different performance aspects, their limitations in addressing fairness and improving metrics through augmentation methods underscore the need for exploring alternative approaches.