expected_cost_loss_acquisition

empulse.metrics.expected_cost_loss_acquisition

Expected cost of a classifier for customer acquisition.

The cost function presumes a situation where leads are targeted either directly or indirectly. Directly targeted leads are contacted and handled by the internal sales team. Indirectly targeted leads are contacted and then referred to intermediaries, which receive a commission. The company gains a contribution from a successful acquisition. y_proba should be (calibrated) probabilities. This metric always returns the average cost per sample.

Methods

All three methods take y_true and y_proba, followed by the keyword-only parameters contribution=7000, contact_cost=50, sales_cost=500, direct_selling=1 and commission=0.1.

__call__(y_true, y_proba, **parameters)

Compute the expected cost of a classifier.

optimal_threshold(y_true, y_proba, **parameters)

Compute the classification threshold(s) that minimize(s) the expected cost.

optimal_rate(y_true, y_proba, **parameters)

Compute the predicted positive rate that minimizes the expected cost.

References

[1]

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 expected_cost_loss_acquisition

y_true = [0, 1, 0, 1, 0, 1, 0, 1]
y_proba = [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9]
expected_cost_loss_acquisition(y_true, y_proba, direct_selling=1)