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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.

Versions across snapshots

VersionRepositoryFileSize
1.0.0 rolling linux/jammy R-4.5 CustomerScoringMetrics_1.0.0.tar.gz 72.7 KiB
1.0.0 rolling linux/noble R-4.5 CustomerScoringMetrics_1.0.0.tar.gz 72.6 KiB
1.0.0 rolling source/ R- CustomerScoringMetrics_1.0.0.tar.gz 16.5 KiB
1.0.0 latest linux/jammy R-4.5 CustomerScoringMetrics_1.0.0.tar.gz 72.7 KiB
1.0.0 latest linux/noble R-4.5 CustomerScoringMetrics_1.0.0.tar.gz 72.6 KiB
1.0.0 latest source/ R- CustomerScoringMetrics_1.0.0.tar.gz 16.5 KiB
1.0.0 2026-04-26 source/ R- CustomerScoringMetrics_1.0.0.tar.gz 16.5 KiB
1.0.0 2026-04-23 source/ R- CustomerScoringMetrics_1.0.0.tar.gz 16.5 KiB
1.0.0 2026-04-09 windows/windows R-4.5 CustomerScoringMetrics_1.0.0.zip 77.2 KiB
1.0.0 2025-04-20 source/ R- CustomerScoringMetrics_1.0.0.tar.gz 16.5 KiB