# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Model Selection and Prediction  
A thorough comparison between many machine learning algorithms was carried out in an effort to identify the best model for the classification of student adaptability levels. These include Multinomial Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), XGBoost, Gradient Boosting, Gaussian Naive Bayes, Support Vector Machine (SVM), Ensemble methods, AdaBoost, CatBoost, and Neural Networks. Tables 2 and 3 include confusion matrices and a variety of performance metrics, providing a comprehensive summary of each model's predictive performance. The three adaptability classes (High, Low, and Moderate) of each model's predictions were broken down in depth by the confusion matrices as shown in Table 3. For example, the Multinomial Logistic Regression model accurately predicted 'High' adaptability 11 times, but it also incorrectly categorized 7 'Moderate' and 5 'Low' instances as 'High'. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Class Name**} & \multicolumn{3}{c}{**Predicted**} \\ \cline{3-5}  & & **High** & **Low** & **Moderate** \\ \hline \multirow{4}{*}{Multinomial Logistic Regression} & High & 11 & 5 & 7 \\ \cline{2-5}  & Low & 1 & 57 & 45 \\ \cline{2-5}  & Moderate & 3 & 14 & 98 \\ \hline \multirow{4}{*}{Decision Tree} & High & 15 & 0 & 8 \\ \cline{2-5}  & Low & 2 & 95 & 6 \\ \cline{2-5}  & Moderate & 1 & 7 & 107 \\ \hline \multirow{4}{*}{Random Forest} & High & 15 & 0 & 8 \\ \cline{2-5}  & Low & 2 & 97 & 4 \\ \cline{2-5}  & Moderate & 0 & 7 & 108 \\ \hline \multirow{4}{*}{K-Nearest Neighbors} & High & 10 & 1 & 12 \\ \cline{2-5}  & Low & 1 & 89 & 13 \\ \cline{2-5}  & Moderate & 2 & 19 & 94 \\ \hline \multirow{4}{*}{XGBoost} & High & 12 & 0 & 11 \\ \cline{2-5}  & Low & 3 & 67 & 33 \\ \cline{2-5}  & Moderate & 2 & 8 & 105 \\ \hline \multirow{4}{*}{Actual} & High & 13 & 0 & 10 \\ \cline{2-5}  & Moderate & 2 & 87 & 14 \\ \cline{2-5}  & Low & 2 & 87 & 14 \\ \cline{2-5}  & Moderate & 0 & 14 & 101 \\ \hline \multirow{4}{*}{Gaussian Naive Bayes} & High & 13 & 3 & 7 \\ \cline{2-5}  & Low & 2 & 62 & 39 \\ \cline{2-5}  & Moderate & 6 & 20 & 89 \\ \hline \multirow{4}{*}{Support Vector Machine (SVM)} & High & 13 & 1 & 9 \\ \cline{2-5}  & Low & 3 & 57 & 43 \\ \cline{2-5}  & Moderate & 4 & 9 & 102 \\ \hline \multirow{4}{*}{Ensemble} & High & 12 & 1 & 10 \\ \cline{2-5}  & Low & 0 & 92 & 11 \\ \cline{2-5}  & Moderate & 0 & 4 & 111 \\ \hline \multirow{4}{*}{AdaBoost} & High & 8 & 3 & 12 \\ \cline{2-5}  & Low & 2 & 69 & 32 \\ \cline{2-5}  & Moderate & 3 & 22 & 90 \\ \hline \multirow{4}{*}{CatBoost} & High & 15 & 0 & 8 \\ \cline{2-5}  & Low & 2 & 95 & 6 \\ \cline{2-5}  & Moderate & 0 & 8 & 107 \\ \hline \multirow{4}{*}{Neural Network} & High & 15 & 0 & 8 \\ \cline{2-5}  & Low & 2 & 93 & 8 \\ \cline{2-5}  & Moderate & 0 & 7 & 108 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion Matrices of the Deployed Models. Understanding the predictive dynamics of each classifier, including their advantages and disadvantages in differentiating between classes, depends on the specificity of the data. To give a complete view of each model's effectiveness, the associated performance measures were generated, including accuracy, precision, recall, and F1 score for each class, as shown in Table 4. The Random Forest model had a strong predictive capacity overall, with precision values of 0.93, 0.94, and 0.94 for the High, Low, and Moderate classes, respectively. These results are further supported by the F1 scores, where high values show a balanced precision-recall trade-off.