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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