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CustomerScoringMetrics  

Evaluation Metrics for Customer Scoring Models Depending on Binary Classifiers
View on CRAN: Click here


Download and install CustomerScoringMetrics package within the R console
Install from CRAN:
install.packages("CustomerScoringMetrics")

Install from Github:
library("remotes")
install_github("cran/CustomerScoringMetrics")

Install by package version:
library("remotes")
install_version("CustomerScoringMetrics", "1.0.0")



Attach the package and use:
library("CustomerScoringMetrics")
Maintained by
Koen W. De Bock
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-04-06
Latest Update: 2018-04-06
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
How to cite:
Koen W. De Bock (2018). CustomerScoringMetrics: Evaluation Metrics for Customer Scoring Models Depending on Binary Classifiers. R package version 1.0.0, https://cran.r-project.org/web/packages/CustomerScoringMetrics. Accessed 22 Dec. 2024.
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