Table 4 Results of the decision tree (DT), logistic regression (LR) and random forest (RF) algorithms, estimated using the different training sets: training (t), under-sampling (u), cost-proportionate rejection-sampling (r) and cost proportionate over-sampling (o)
From: A novel cost-sensitive framework for customer churn predictive modeling
Algorithm | Set | Savings | F 1 S c o r e |
|---|---|---|---|
DT | t | -0.0001 ± 0.0193 | 0.0750 ± 0.0199 |
u | -0.0370 ± 0.0603 | 0.1177 ± 0.0108 | |
r | 0.0018 ± 0.0549 | 0.1200 ± 0.0129 | |
o | 0.0249 ± 0.0203 | 0.1019 ± 0.0189 | |
LR | t | -0.0001 ± 0.0002 | 0.0000 ± 0.0000 |
u | 0.0062 ± 0.0487 | 0.1227 ± 0.0097 | |
r | 0.0500 ± 0.0372 | 0.1260 ± 0.0112 | |
o | 0.0320 ± 0.0225 | 0.1088 ± 0.0199 | |
RF | t | -0.0026 ± 0.0081 | 0.0245 ± 0.0148 |
u | 0.0424 ± 0.0547 | 0.1342 ± 0.0113 | |
r | 0.1033 ± 0.0402 | 0.1443 ± 0.0127 | |
o | 0.0205 ± 0.0161 | 0.0845 ± 0.0204 |