## 4 Results and Discussion
### Predictive Model Results
#### 4.2.4 Model Training and Scoring  
The model training and evaluation phase covers the model building, testing, comparison and selection processes; it is designed to find the best performing model given a set of data. Once the model has been trained, it can be used to make predictions. Models are trained using an imbricated framework of cross validation and hyperparameter optimization. Different learning algorithms and parameters need to be trained and optimized to compare their performance and select the best performing model. It is recommended to consider the mathematical nature of the dropout prediction algorithms, the time of the prediction, as well as the potential accuracy, which is related to the complexity of the model, to ensure quality control; however, factors such as the volume, variety, and data speed influence the performance of the algorithms [22]. In addition, the comparison of the predicted class with the actual class allows distinguishing between true positive and true negative classification and false negative and false positive classification. This distinction enables the calculation of several quality statistics [23]. In the modeling process, 70% of the dataset was utilized for training, while the remaining 30% was utilized for testing. Various machine learning methods were applied to the dataset to evaluate and compare the performance, such as LR, KNN, SVM, and DTree. The classification algorithms' efficiency was assessed using the confusion matrix provided below (see Table 10). The provided information includes metrics related to the prediction model dropout, which are defined as follows (see Table 10):  
* True positive: this refers to the number of students correctly identified as motivated to finish their education. * True negative: this refers to the number of students correctly identified as unmotivated or likely to drop out. * False positive: this refers to the number of students predicted as unmotivated or likely to drop out but who are actually motivated. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{2-3}  & **Yes** & **No** \\ \hline Yes & True positive & False negative \\ \hline No & False positive & True negative \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confusion matrix. * False negative: this refers to the number of students predicted as motivated to complete but who are actually not motivated. Figure 8: Correlation heatmap. (**a**) Correlation matrix of all numerical features; (**b**) correlation of features with student motivation. Performance evaluation  
In binary classification, the confusion matrix provides a fundamental basis for evaluating model performance. One of the frequently used metrics is accuracy [10]. It measures the overall proportion of correctly classified instances in a dataset. It is measured as the number of correct predictions divided by the total number of predictions. \[accuracy=\frac{a+d}{a+b+c\ +d} \tag{1}\]  
where  
**a**: true positives (TP),  
**b**: false positives (FP),  
**c**: false negatives (FN),  
**d**: true negatives (TN). However, this measure is inappropriate in situations where classes are not represented in a balanced way, because assigning all examples to the misrepresented class would result in high accuracy but little added value for class differentiation [24]. As a result, more appropriate evaluation metrics can used to solve this restriction, such as in Ref. [25]. Precision measures the proportion of positive predictions that are correct. It indicates how many of the instances predicted as positive are truly positive. \[Precision=\frac{a}{a+b} \tag{2}\]  
Recall is the proportion of actual positive instances that a classification model correctly identifies. It indicates how effectively the model captures all relevant positive cases.