Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

@article{Bock2012ReconcilingPA,
  title={Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models},
  author={Koen W. De Bock and Dirk Van den Poel},
  journal={Expert Syst. Appl.},
  year={2012},
  volume={39},
  pages={6816-6826}
}
To build a successful customer churn prediction model, a classification algorithm should be chosen that fulfills two requirements: strong classification performance and a high level of model interpretability. In recent literature, ensemble classifiers have demonstrated superior performance in a multitude of applications and data mining contests. However, due to an increased complexity they result in models that are often difficult to interpret. In this study, GAMensPlus, an ensemble classifier… CONTINUE READING
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