# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 4 Experiments and Evaluation
### Comparative Analysis of Applied Techniques  
A comparative analysis was performed to analyze the performance of different classifiers evaluated during this study. To analyze the performance, the comparison of the evaluation results of single classifiers, ensemble-based classifiers, and fusion-based ensemble models is presented in this section. First, this section presents the comparison between single-based models and ensemble-based models. Secondly, a comparison is performed between fusion ensemble-based models. The experimental results shown in Figure 7 depict that all of the ensemble-based models outperformed concerning all measures including accuracy, recall, precision, and F-score in comparison with single-based classifiers. Figure 7 shows the performance of models through bar charts and numeric terms. The purpose of this experiment was to identify the high-performing fusion model by comparing fusion-based models. The results showed that fusion-based models improve the precision and accuracy of the student prediction model. Figure 8 presents the performance of classifiers using a bar chart and table of numeric terms. The results shown in the above figure indicate that MB and MLP performed very well in terms of all measures. The fusion model MB + MLP achieved 98.7 accuracy and 98.6% precision, recall, and F-score which is higher than those for all other fusion models. The rest of the fusion models also showed relatively good performance. Overall, the results showed that fusion-based models improve the accuracy and precision of student predictions in comparison to single- and ensemble-based models. For effective communication, it is to be considered that the false positive rate, also known as the rate of sensitivity, should be nearly zero. The sensitivity rate for the MB + MLP model can be seen very close to zero, which strengthens the performance of the model. Figure 6: Voting with single-based classifiers. The F-measure of fusion-based MultiBoostAB and MLP has produced an average of 0.987 score that is a very significant result. It gives an average weight of the true positive as 0.988 and false positive rate as 0.004, as shown in Table 3.  
As an average, the overall accuracy rate of the proposed model is evaluated as 98.7%. It shows that fusion-based ensemble models provide better precise outcomes, while classifying instances is very helpful in the evaluation of students' performance. Figure 8: Comparison of fusion-based ensemble classifiers. Figure 7: Comparison of single and ensemble-based classifiers. Figure 9 below shows the fusion-based ensemble model of ninth-class grade division based on the performance of MB + MLP and the class column represents the final performance prediction grades of the ninth class. In machine learning, sensitivity or recall is termed as the true positive rate and is used to measure the percentages of actual positives, which are identified correctly. The sensitivity rate should be nearer to one in order to obtain the true positive values. As shown in the above figure, the A+ grade has a true positive value of 290, whereas the false negative and false positive values are 7 and 1. The A grade has a true positive value of 350 with 8 false negative and positive values. Similarly, the values including 169, 223, 93, 33 and 30 are the true positive values of grades B+, B, C, D, and F.  
The false-positive ratio (FPR) is the proportion of incorrectly classified negative instances. It has been discovered that a good model should have a false positive rate nearer to 0.0, which indicates less incorrectly classified negative instances. A confusion matrix is a technique in which the performance of a classifier is summarized. From the confusion matrix, it was derived that, amongst the students from the dataset, 24.1% students secure an A+ grade, 18.5% students achieve a B grade, 14.0%, 29.1%, 7.7%, 2.4% and 2.7% students secure B+, A, C, F, D grades, respectively. This distribution obtains more insight from the model analysis.