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International Journal of Educational Technology in Higher Education

Table 9 Test statistics of the top five candidate ensemble models with optimization

From: Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Candidate ensemble and its
sub-models 		TN FP FN TP
ET + RF + LR 131 14 110 359
QDA + LR 121 24 89 380
GB + ET + RF + LR 121 24 91 378
GB + DT + LR 117 28 90 379
GB + DT + LR + ANN 118 27 91 378
  1. Italic characters show highest true negative prediction.
  2. Gradient Boosting (GB), Quadratic Discriminant Analysis (QDA), Extra Trees (ET), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Artificial Neural Network (ANN)
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