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Validation of Net Models: Measuring Stability and Discriminatory Power

In: Targeting Uplift

Author

Listed:
  • René Michel

    (Deutsche Bank AG)

  • Igor Schnakenburg

    (DeTeCon International GmbH)

  • Tobias von Martens

    (Deutsche Bank AG)

Abstract

Measuring the quality of scoring models is a mandatory and crucial step in the data mining process. This chapter suggests key performance indicators of model quality that have been transferred from classical (gross) scoring or were specifically designed for net scoring. For model stability, the average squared deviation, a significance-based measure, and the model stability rank correlation are regarded as appropriate, whereas for discriminatory power, Qini, AUnROC, and a significance-based measure should be used. Subsequently, the validation and adjustment of uplift models over time are explained.

Suggested Citation

  • René Michel & Igor Schnakenburg & Tobias von Martens, 2019. "Validation of Net Models: Measuring Stability and Discriminatory Power," Springer Books, in: Targeting Uplift, chapter 0, pages 101-120, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-22625-1_4
    DOI: 10.1007/978-3-030-22625-1_4
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