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Table 10 Performance scores of individual algorithms and ensemble model

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

  GB QDA ET DT RF LR ANN ELM
Specificity 0.6276 0.5586 0.7724 0.8276 0.8621 0.8345 0.5931 0.9034
Sensitivity 0.9190 0.9467 0.7719 0.4670 0.7868 0.8102 0.9446 0.7655
Precision 0.8887 0.8740 0.9165 0.8975 0.9486 0.9406 0.8825 0.9625
CV—Specificity 0.6099 0.5768 0.7841 0.8445 0.8425 0.8362 0.5768 0.8861
  1. Gradient Boosting (GB), Quadratic Discriminant Analysis (QDA), Extra Trees (ET), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Ensemble Learning Model (ELM)