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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%