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Table 5 Results for gender prediction

From: Predicting customer’s gender and age depending on mobile phone data

Algorithm Accuracy AUC Classification time (s)
XGBoost 0.8558 0.9226 0.3988
Logistic regression 0.8062 0.8727 0.3594
Naive Bayes 0.7084 0.7406 11.1174
Random forest 0.8396 0.9011 0.5021
GBM 0.8415 0.9039 0.9394
Bagged CART 0.8379 0.901 1.3061
GLMNET 0.7927 0.8669 0.2447
KNN 0.7233 0.7641 9.9087
C5.0 0.8317 0.9023 1.4531
CART 0.7295 0.7627 0.4628
LDA 0.7813 0.8543 12.6713
SVM 0.8149 0.8796 1.5702