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 |