Table 3 Accuracy and AUC value of the selected machine algorithm after data balancing and tuning.

From: Employing machine learning techniques for prediction of micronutrient supplementation status during pregnancy in East African Countries

Models

Accuracy

AUC

SVM

86.0%

0.721

Gaussian naive baye

77.0%

0.651

Logistic regression

74.0%

0.683

Decision tree classifier

92.0%

0.862

Random forest classifier

94.0%

0.892

Gradient boosting classifier

86.0%

0.739

XGBoost

92.0%

0.856

KNN

91.0%

0.797

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