| name | ce-mondrian-conditional |
| description | Configure and validate Mondrian and conditional calibration for subgroup-aware uncertainty and fairness-sensitive workflows.
|
CE Mondrian Conditional
You are setting up Mondrian (conditional) calibration, which partitions
calibration data into subgroups so that each group receives its own
uncertainty estimate. This reveals group-specific prediction quality and is
the foundational technique for fairness-aware deployments in CE.
Research: Conditional Calibrated Explanations (xAI 2024)
Load references/mondrian_examples.md for full code examples (Options A/B/C,
fairness analysis, global vs conditional comparison).
Why Mondrian matters for fairness
Without conditional calibration, the CPS/Venn-Abers calibrator averages over
all calibration instances. A minority group with harder prediction patterns
may receive the same interval width as an easy majority group, hiding bias.
Mondrian splits calibration by a grouping key and fits a separate calibrator
per bin. Resulting intervals are:
- Narrower for groups the model predicts reliably.
- Wider for groups the model predicts poorly.
Three options for specifying bins
- Option A — Inline
bins array: pass integer group labels directly at calibrate time.
- Option B —
MondrianCategorizer (recommended for continuous features): auto-bins
via crepes.extras.MondrianCategorizer.
- Option C — Lambda as
mc: pass a callable directly for ad-hoc one-off scripts.
Calibration -> predict -> explain consistency rules
| Step | Bins argument |
|---|
calibrate(...) | mc= (MondrianCategorizer or callable) OR bins= (integer array) |
predict(x, ...) | nothing if mc was used at calibrate time OR bins=group_labels_test |
predict_proba(x, ...) | same as above |
explain_factual(x, ...) | same as above |
explore_alternatives(x, ...) | same as above |
CRITICAL: always pass bins= at inference time whenever the explainer
was calibrated with Mondrian bins. Omitting it silently falls back to global
calibration, which defeats fairness analysis.
Minimum bin size warning
Mondrian calibration splits the calibration set by group. Too few samples
per bin leads to unreliable or very wide intervals.
Rule of thumb: aim for >= 30-50 calibration samples per bin.
Out of Scope
- DifficultyEstimator (per-instance sigma scaling for regression; see
ce-regression-intervals).
- Reject policies (deciding whether to defer uncertain predictions; see
ce-reject-policy).
- Fairness constraint enforcement (CE reveals uncertainty; it does not enforce fairness automatically).
Evaluation Checklist
