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.

Version:	1.0.0
Published:	2018-04-06
DOI:	10.32614/CRAN.package.CustomerScoringMetrics
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

Documentation:

Reference manual:

CustomerScoringMetrics.pdf

Downloads:

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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