CE Reject Policy

You are configuring reject / defer logic for production decision pipelines. Reject policies let the framework flag, skip, or selectively process instances where the model's prediction is ambiguous, novel, or uncertain — all without modifying the underlying model.

ADR reference: ADR-029 (Reject Integration Strategy).

Load references/reject_policy_examples.md for full code examples.

The four policies

from calibrated_explanations.core.reject.policy import RejectPolicy

Legacy policy strings (deprecated, emit DeprecationWarning):

Use the enum members directly, not string values, to avoid deprecation warnings.

RejectResult envelope

When any non-NONE policy is active, the return type changes to a RejectResult:

from calibrated_explanations.explanations.reject import RejectResult

result.prediction   # calibrated predictions (array or None if policy skips them)
result.explanation  # CalibratedExplanations or None
result.rejected     # boolean mask: True = rejected, False = accepted
result.policy       # RejectPolicy member that generated this result
result.metadata     # dict with telemetry: error_rate, reject_rate, etc.

Policy selection guide

Out of Scope

• Mondrian group calibration (see ce-mondrian-conditional).
• Calibrated predictions without reject logic (see ce-calibrated-predict).
• Guarded factual explanations (explain_guarded_factual — legacy interface, use explain_factual + reject policy instead).

Evaluation Checklist

• RejectPolicy enum member used (not deprecated string value).
• Return type checked: RejectResult when policy != NONE, plain type otherwise.
• result.rejected mask inspected for actual rejection counts.
• Initialization failure path tested (metadata["init_error"]).
• Per-call override tested alongside explainer-level default.
• Regression: initialize_reject_learner(threshold=t) called before predict_reject.
• Tests use pytest.warns(UserWarning) when fallback warning is expected.