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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.
| Version: | 1.0.0 |
| Published: | 2018-04-06 |
| Author: | Koen W. De Bock |
| Maintainer: | Koen W. De Bock <kdebock at audencia.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | CustomerScoringMetrics results |
| Reference manual: | CustomerScoringMetrics.pdf |
| Package source: | CustomerScoringMetrics_1.0.0.tar.gz |
| Windows binaries: | r-devel: CustomerScoringMetrics_1.0.0.zip, r-release: CustomerScoringMetrics_1.0.0.zip, r-oldrel: CustomerScoringMetrics_1.0.0.zip |
| macOS binaries: | r-release (arm64): CustomerScoringMetrics_1.0.0.tgz, r-oldrel (arm64): CustomerScoringMetrics_1.0.0.tgz, r-release (x86_64): CustomerScoringMetrics_1.0.0.tgz, r-oldrel (x86_64): CustomerScoringMetrics_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
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