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
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
2026-04-09 windows/windows R-4.5 | CustomerScoringMetrics_1.0.0.zip |
77.2 KiB |