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.

Getting started

Package details

AuthorKoen W. De Bock
MaintainerKoen W. De Bock <[email protected]>
LicenseGPL (>= 2)
Version1.0.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("CustomerScoringMetrics")

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CustomerScoringMetrics documentation built on May 2, 2019, 5:17 a.m.