mpcs_score

empulse.metrics.mpcs_score

Maximum Profit measure for Credit Scoring (MPCS).

MPCS 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 fraction of the loan amount being lost. For detailed information, consult the paper [1].

See empcs_score for a stochastic version of this metric.

The MP measure for Credit Scoring is defined as [1]:

\[\max_t \lambda \pi_0 F_0(t) - ROI \pi_1 F_1(t)\]

The MP 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).

Methods

__call__(y_true, y_score, *, loan_lost_rate=0.275, roi=0.2644)

Compute the maximum profit that can be achieved by a classifier at its optimal decision threshold.

optimal_threshold(y_true, y_score, *, loan_lost_rate=0.275, roi=0.2644)

Compute the classification threshold that maximizes the profit.

optimal_rate(y_true, y_score, *, loan_lost_rate=0.275, roi=0.2644)

Compute the predicted positive rate (fraction of loan applications that should be accepted) at which the maximum profit is achieved.

References

[1] (1,2)

Verbraken, T., Bravo, C., Weber, R., & 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.

Examples

from empulse.metrics import mpcs_score

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]
mpcs_score(y_true, y_score, loan_lost_rate=0.275, roi=0.2644)