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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