FAQ

Do I need to retrain my model?

No. error-parity is a postprocessing method that wraps a score-based predictor.

What if my groups are not 0..G-1?

Encode them to integers starting at 0 before calling fit or prediction. Noncontiguous encodings raise a ValueError.

Can I use decision_function instead of predict_proba?

Yes, pass predictor=lambda X: model.decision_function(X). Ensure higher values indicate higher likelihood of the positive class.

What tolerance should I use?

Start with tolerance=0.0 (strict). Increase gradually to explore trade-offs using the postprocessing curve utilities.

How do I get uncertainty estimates?

Use error_parity.evaluation.evaluate_predictions_bootstrap() and the plotting utilities for confidence intervals on frontiers.