Package: CustomerScoringMetrics 1.0.0

CustomerScoringMetrics: Evaluation Metrics for Customer Scoring Models Depending on Binary Classifiers

Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.

Authors:Koen W. De Bock

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CustomerScoringMetrics.pdf |CustomerScoringMetrics.html✨
CustomerScoringMetrics/json (API)

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13 exports 0.00 score 0 dependencies 22 scripts 179 downloads

Last updated 6 years agofrom:09f407d8ba. Checks:OK: 7. Indexed: yes.

Exports:checkDepVectorconfMatrixMetricscumGainsChartcumGainsTablecutoffSensitivityPlotdynAccuracydynConfMatrixexpMisclassCostliftChartliftIndexliftTablemisclassCosttopDecileLift

Dependencies: