empCreditScoring {EMP} | R Documentation |
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.
empCreditScoring(scores, classes, p0=0.55, p1=0.1, ROI=0.2644)
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. |
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. |
Cristian Bravo, Seppe vanden Broucke and Thomas Verbraken.
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 empChurn
, prediction
.
# 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)