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Table 2 P, R, and F1 scores of eleven user purchasing behavioral prediction models

From: Application of sample balance-based multi-perspective feature ensemble learning for prediction of user purchasing behaviors on mobile wireless network platforms

Evaluation index P R F1 score
Model type
Comparison model co-EM-LR 0.0734 ± 0.0025 0.0697 ± 0.0026 0.0715 ± 0.0046
GDBT 0.0822 ± 0.0010 0.0789 ± 0.0035 0.0808 ± 0.0044
MMSE 0.1221 ± 0.0041 0.0983 ± 0.0064 0.1089 ± 0.0055
LR-XGBoost 0.1104 ± 0.0032 0.1048 ± 0.0024 0.1075 ± 0.0045
RNN 0.0874 ± 0.0047 0.0783 ± 0.0054 0.0826 ± 0.0041
MLP-LSTM 0.0852 ± 0.0019 0.0708 ± 0.0065 0.0773 ± 0.0021
Ablation model FCVS without XL 0.1704 ± 0.0054 0.1311 ± 0.0012 0.1482 ± 0.0042
FCVS without LL 0.1652 ± 0.0036 0.1458 ± 0.0024 0.1549 ± 0.0012
FCVS without CDC 0.1557 ± 0.0054 0.1326 ± 0.0041 0.1432 ± 0.0028
Stacking 0.1502 ± 0.0029 0.1835 ± 0.0050 0.1652 ± 0.0036
FCVS 0.1714 ± 0.0013 0.1871 ± 0.0027 0.1789 ± 0.0024