EMP-package: Expected Maximum Profit Classification Performance Measure

In EMP: Expected Maximum Profit Classification Performance Measure

Expected Maximum Profit Classification Performance Measure

Description

The EMP measure is an alternative to AUC that includes the expected profit of a given model, when compared to a baseline (no model used). Presented in Verbraken et al. (2014) as a preferred measure for credit risk scoring in any profit-driven environment and in Verbraken et al. (2013) as a measure for customer churn prediction. For credit scoring, this implementation assumes an LGD distribution with two point masses, and a constant ROI. For churn prediction, this implementation assumes a beta distribution and a constant CLV.

Details

Package:	EMP
Type:	Package
Version:	2.0.6
Date:	2025-05-07
License:	GPL (>=3)

The package exports only two functions, empCreditScoring and empChurn.

Author(s)

Authors: Cristian Bravo, Seppe vanden Broucke and Thomas Verbraken. Mantainer: Cristian Bravo <[e-mail]>.

References

Verbraken, T., Wouter, V. and Baesens, B. (2013). A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models. Knowledge and Data Engineering, IEEE Transactions on. 25 (5): 961-973. Available Online: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1109/TKDE.2012.50")} Verbraken, T., Bravo, C., Weber, R. and Baesens, B. (2014). Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research. 238 (2): 505 - 513. Available Online: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ejor.2014.04.001")}

Examples

# Construct artificial probability scores and true class labels
score.ex <- runif(1000, 0, 1)
class.ex <- unlist(lapply(score.ex, function(x){rbinom(1,1,x)}))

# Calculate EMP measures for credit risk scoring
empCreditScoring(score.ex, class.ex)

# Calculate EMP measures for customer churn prediction
empChurn(score.ex, class.ex)

EMP documentation built on June 8, 2025, 1:52 p.m.