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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