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name: postprocessing description: "Use Fairlearn ThresholdOptimizer and threshold-optimizer plots to adjust trained predictors under group fairness constraints." disable-model-invocation: true metadata: disco-role: operating license: MIT

Fairlearn postprocessing

Use this sub-skill when the task asks to adjust a trained predictor's scores or predictions with group-specific thresholds: ThresholdOptimizer, plot_threshold_optimizer, prefit, predict_method, tol, demographic parity, equalized odds, false/true positive/negative rate parity, or threshold interpolation dictionaries.

Quick workflow

1. Choose or fit a base estimator that exposes a useful score method: predict_proba, decision_function, or predict.
2. Create ThresholdOptimizer(estimator=..., constraints=..., objective=..., predict_method=...).
3. Set prefit=True only when the estimator is already fitted and will not be cloned by cross-validation.
4. Fit the threshold optimizer with sensitive_features=....
5. Predict with the same sensitive-feature grouping and, when deterministic output matters, pass random_state to predict.
6. Route to ../assessment/ to compare subgroup metrics before and after postprocessing.

Read these references

• references/threshold-optimizer-workflows.md for constructor options, constraints/objectives, prefit, score-method choice, plotting, and evaluation patterns.
• references/troubleshooting.md for missing base estimators, invalid constraint/objective combinations, plotting errors, prefit warnings, and data-layout failures.
• scripts/smoke_threshold_optimizer.py for a tiny CPU-only synthetic smoke check with optional plotting.

Core APIs to recognize

• ThresholdOptimizer(*, estimator=None, constraints="demographic_parity", objective="accuracy_score", grid_size=1000, flip=False, prefit=False, predict_method="auto", tol=None)
• ThresholdOptimizer.fit(X, y, *, sensitive_features, **kwargs)
• ThresholdOptimizer.predict(X, *, sensitive_features, random_state=None)
• plot_threshold_optimizer(threshold_optimizer, ax=None, show_plot=True)

Boundary rules

• This sub-skill owns post-fit threshold/interpolation mitigation. Use ../reductions/ for fairness-constrained retraining and ../preprocessing/ for feature transformation.
• Use ../assessment/ for grouped metrics and model-comparison plots. plot_threshold_optimizer visualizes the postprocessor internals, not a full fairness report.
• Use ../installation/ or root troubleshooting when matplotlib is missing.
• Use ../adversarial/ for neural adversarial fairness; it is not a threshold optimizer.

Operating rules

• y must be binary for ThresholdOptimizer in this source.
• sensitive_features are required for both fit and predict.
• Prefer predict_proba or decision_function over hard-label predict when meaningful scores are available.
• equalized_odds does not support tol relaxation in the inspected source.
• prefit=True is unsafe with estimator cloning workflows such as cross_val_score or GridSearchCV; use prefit=False there.
• Postprocessing can change output randomness. Use random_state in predict for reproducible reports.

Fast validation

Run:

python sub-skills/postprocessing/scripts/smoke_threshold_optimizer.py

Run plot coverage in a matplotlib-capable environment:

python sub-skills/postprocessing/scripts/smoke_threshold_optimizer.py --plot --output-dir /tmp/fairlearn-threshold-plots