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Table 16 Analysis of the models

From: Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique

Model Accuracy Avg. Precision Avg. Recall Avg. F-Measure Avg. AUC
Naive Bayes 61.11% 56.40% 53.30% 51.58% 75.6%
Naive Bayes (optimal binning) 68.33% 60.86% 60.35% 60.26% 68.9%
Naive Bayes (SMOTE) 66.67% 72.96% 66.67% 64.58% 81.4%
Naive Bayes (optimal binning + SMOTE) 75.28% 75.30% 75.58% 75.13% 71.8%
Decision tree 56.11% 56.90% 45.73% 43.44% 40.1%
Decision tree (optimal binning) 60.56% 50.56% 48.96% 49.54% 47.9%
Decision tree (SMOTE) 70.83% 72.48% 70.87% 70.61% 64.8%
Decision tree (optimal binning + SMOTE) 70.56% 70.91% 71.00% 70.03% 68.4%
Neural net 65.56% 70.21% 60.41% 62.65% 72.3%
Neural net (optimal binning) 68.89% 66.62% 69.89% 67.69% 73.1%
Neural net (SMOTE) 73.61% 73.54% 73.38% 73.59% 81.3%
Neural net (Optimal Binning + SMOTE) 75.28% 75.63% 75.42% 75.48% 71.6%