expected_cost_loss_churn

empulse.metrics.expected_cost_loss_churn

Expected cost of a classifier for customer churn.

The cost function presumes a situation where identified churners are contacted and offered an incentive to remain customers. Only a fraction of churners accepts the incentive offer. For detailed information, consult the paper [1]. y_proba should be (calibrated) probabilities. This metric always returns the average cost per sample.

See also

B2BoostClassifier : Uses this metric as the training objective.

Methods

__call__(y_true, y_proba, *, accept_rate=0.3, clv=200, incentive_fraction=0.05, contact_cost=1)

Compute the expected cost of a classifier.

optimal_threshold(y_true, y_proba, *, accept_rate=0.3, clv=200, incentive_fraction=0.05, contact_cost=1)

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

optimal_rate(y_true, y_proba, *, accept_rate=0.3, clv=200, incentive_fraction=0.05, contact_cost=1)

Compute the predicted positive rate that minimizes the expected cost.

References

[1]

Janssens, B., Bogaert, M., Bagué, A., & Van den Poel, D. (2022). B2Boost: Instance-dependent profit-driven modelling of B2B churn. Annals of Operations Research, 1-27.

Examples

from empulse.metrics import expected_cost_loss_churn

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_churn(
    y_true, y_proba, accept_rate=0.3, clv=200, incentive_fraction=0.05, contact_cost=1
)