Table 5 Results of the decision tree (DT), logistic regression (LR) and random forest (RF) algorithms, estimated using the different training sets
From: A novel cost-sensitive framework for customer churn predictive modeling
Algorithm | Set | Savings | F 1 S c o r e |
|---|---|---|---|
DT - BMR | t | 0.0303 ± 0.0148 | 0.0946 ± 0.0158 |
u | 0.0574 ± 0.0387 | 0.1095 ± 0.0203 | |
r | 0.0652 ± 0.0365 | 0.1151 ± 0.0169 | |
o | 0.0306 ± 0.0149 | 0.0924 ± 0.0184 | |
LR - BMR | t | 0.1058 ± 0.0319 | 0.1361 ± 0.0154 |
u | 0.0963 ± 0.0388 | 0.1319 ± 0.0166 | |
r | 0.0823 ± 0.0364 | 0.1240 ± 0.0153 | |
o | 0.0986 ± 0.0287 | 0.1333 ± 0.0149 | |
RF - BMR | t | 0.0835 ± 0.0349 | 0.1252 ± 0.0151 |
u | 0.1300 ± 0.0368 | 0.1429 ± 0.0127 | |
r | 0.1336 ± 0.0348 | 0.1429 ± 0.0132 | |
o | 0.0907 ± 0.0359 | 0.1275 ± 0.0136 |