EMP (version 2.0.5)

empCreditScoring: empCreditScoring

Description

Estimates the EMP for credit risk scoring, considering constant ROI and a bimodal LGD function with point masses p0 and p1 for no loss and total loss, respectively.

Usage

empCreditScoring(scores, classes, p0=0.55, p1=0.1, ROI=0.2644)

Arguments

scores

A vector of predicted probabilities.

classes

A vector of true binary class labels.

p0

Percentage of cases on the first point mass of the LGD distribution (complete recovery).

p1

Percentage of cases on the second point mass of the LGD distribution (complete loss).

ROI

Constant ROI per granted loan. A percentage.

Value

An EMP object with two components.

EMP

The Expected Maximum Profit of the ROC curve at EMPfrac cutoff.

EMPfrac

The percentage of cases that should be excluded, that is, the percentual cutoff at EMP profit.

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: http://ieeexplore.ieee.org/iel5/69/6486492/06165289.pdf?arnumber=6165289 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: http://www.sciencedirect.com/science/article/pii/S0377221714003105

See Also

See Also empChurn, prediction.

Examples

# NOT RUN {
# 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 credit risk scoring with point masses
# in 0.1 and 0.9, and 0.1 ROI
empCreditScoring(score.ex, class.ex, 0.1, 0.1, 0.1)
# }