empcs#
- empulse.metrics.empcs(y_true, y_score, *, success_rate=0.55, default_rate=0.1, roi=0.2644, check_input=True)[source]#
Expected Maximum Profit measure for Credit Scoring.
EMPCS presumes a situation where a company is considering whether to grant a loan to a customer. Correctly identifying defaulters results in receiving a return on investment (ROI), while incorrectly identifying non-defaulters as defaulters results in a loss of the loan amount. The degree to which the loan is lost is determined by the probability that the entire loan is lost (
default_rate), probability that the entire loan is paid back (success_rate), and a uniform distribution of partial loan losses (1 - default_rate - success_rate). For detailed information, consult the paper [1].See also
empcs_score: to only return the EMPCS score.mpcs: for a deterministic version of this metric.- Parameters:
- y_true1D array-like, shape=(n_samples,)
Binary target values (‘acquisition’: 1, ‘no acquisition’: 0).
- y_score1D array-like, shape=(n_samples,)
Target scores, can either be probability estimates or non-thresholded decision values.
- success_ratefloat, default=0.55
Probability that the entire loan is paid back (
0 ≤ succes_rate ≤ 1).- default_ratefloat, default=0.1
Probability that the entire loan is lost (
0 ≤ default_rate ≤ 1).- roifloat, default=0.2644
Return on investment on the loan (
roi ≥ 0).- check_inputbool, default=True
Perform input validation. Turning off improves performance, useful when using this metric as a loss function.
- Returns:
- empcsfloat
Expected Maximum Profit measure for customer Credit Scoring
- thresholdfloat
Fraction of loan applications that should be accepted to maximize profit
Notes
The EMP measure for Credit Scoring is defined as [1]:
\[\int_0^1 \lambda \pi_0 F_0(T) - ROI \pi_1 F_1(T) \cdot h(\lambda) d\lambda\]The EMP measure for Credit Scoring requires that the default class is encoded as 0, and it is NOT interchangeable. However, this implementation assumes the standard notation (‘default’: 1, ‘no default’: 0).
References
Examples
>>> from empulse.metrics import empcs >>> >>> y_true = [0, 1, 0, 1, 0, 1, 0, 1] >>> y_score = [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9] >>> empcs(y_true, y_score) (0.09747017050000001, 0.32434500000000005)