Package: CustomerScoringMetrics
Type: Package
Title: Evaluation Metrics for Customer Scoring Models Depending on
        Binary Classifiers
Version: 1.0.0
Author: Koen W. De Bock
Maintainer: Koen W. De Bock <kdebock@audencia.com>
Description: 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.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2026-06-17 08:30:55 UTC; root
Repository: https://koendebock.r-universe.dev
Date/Publication: 2018-04-06 09:39:01 UTC
RemoteUrl: https://github.com/cran/CustomerScoringMetrics
RemoteRef: HEAD
RemoteSha: 09f407d8ba99ad86e146dba7af98d4ad0c2d9189