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.


Reference manual

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install.packages("CustomerScoringMetrics")

1.0.0 by Koen W. De Bock, 8 years ago


Browse source code at https://github.com/cran/CustomerScoringMetrics


Authors: Koen W. De Bock


Documentation:   PDF Manual  


GPL (>= 2) license



See at CRAN