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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)