## 4 Results
### Results of the Predictive Performance Evaluation  
An experiment was carried out for each of the selected machine learning methods, based on 30 repetitions of the Hold-out validation scheme; 80% of the data were used for training and 20% for testing. In addition, the data separation was marked by the Shuffle Split technique, which performs a random permutation of the dataset records to form the training and test sets [37; 38]. After the training and testing process of the selected classifiers, the results were obtained in Table 2. Almost all the machine learning models performed well, surpassing 86% Accuracy, with the exception of QDA, which obtained only 46.0% in that indicator. In this sense, the XGBoost classifier is recommended, as it shows the best performance indicators, mainly in Accuracy, reaching 0.912, with a good balance between Precision 0.9263 and Recall 0.9454, and an F1-score of 0.9358. On the other hand, although there are classifiers with similar performance to the XGBoost in terms of Precision, Recall, and F1-score, these differences are minimal and considered negligible. However, the LDA classifier stands out in Recall (0.9696), indicating that this classifier better identifies students with good academic performance. On the other hand, the MLP showed better Precision (0.9268). One of the possible causes that the considered classifiers obtain good performance is the separability of the Label attribute (classes), because as shown in Figure 9, if the Failed Courses and Second Year Score variables are considered, it is observed that the classes can \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline XGBoost & 0.912 \(\pm\) 0.0072 & 0.9358 & 0.9263 & 0.9454 \\ Linear SVM & 0.909 \(\pm\) 0.0071 & 0.9359 & 0.9243 & 0.9478 \\ Linear Reg & 0.908 \(\pm\) 0.0075 & 0.9338 & 0.9180 & 0.9503 \\ Decision Tree & 0.905 \(\pm\) 0.0089 & 0.9322 & 0.9228 & 0.9418 \\ Random Forest & 0.904 \(\pm\) 0.0080 & 0.9280 & 0.9253 & 0.9309 \\ LDA & 0.901 \(\pm\) 0.0091 & 0.9269 & 0.8879 & 0.9696 \\ RBF SVM & 0.900 \(\pm\) 0.0077 & 0.9315 & 0.9147 & 0.9490 \\ MLP & 0.899 \(\pm\) 0.0382 & 0.9312 & 0.9267 & 0.9357 \\ Gradient Boost & 0.893 \(\pm\) 0.0097 & 0.9257 & 0.9218 & 0.9296 \\ Perceptron & 0.880 \(\pm\) 0.0119 & 0.9121 & 0.9177 & 0.9066 \\ K-NN & 0.862 \(\pm\) 0.0102 & 0.9062 & 0.8671 & 0.9490 \\ QDA & 0.460 \(\pm\) 0.0657 & 0.2677 & 0.8904 & 0.1575 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the evaluation of the machine learning models with a cross-validation approach of 30 executions. be separated even with a straight line. Although, the linear classifiers such as LDA, Linear SVM, and Linear Regression show good performance. In terms of the importance of student attributes in predicting academic performance, the classifiers XGBoost, Random Forest, and Decision Tree assign coefficients to student features in such a way that the greater the value of the coefficient, the greater the weight or importance of the feature in the target. In order to interpret other types of classifiers, the shap-values [39] technique was used. This technique allows calculating coefficients, called shap-values, for any classifier and determining the features that have the greatest weight in the predictive model generated by it. In this way, it is possible to superficially understand the underlying model generated by the classifier and to know the features that are most important or most related to the value of the predictions. In this context, Figure 10 presents the shap-values associated with the predictive model generated by the XGBoost classifier, which achieved the highest performance indicators. In Figure 10, a set of points is distributed according to their shap-value on a specific number line, for the 20 most important characteristics for the prediction of the model generated with XGBoost.