name: sklearn-interface
description: "Use AIF360's preferred sklearn-compatible pandas API for datasets,
fairness metrics, scorers, estimators, and pipeline caveats."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
AIF360 sklearn Interface
Use this sub-skill when the task is best handled through the preferred aif360.sklearn API: pandas DataFrame/Series inputs, protected attributes stored in indexes, sklearn-compatible fairness metrics/scorers, estimators, meta-estimators, and pipeline caveats.
Fast routing
Boundaries and handoffs
- Route legacy
BinaryLabelDataset, StructuredDataset, StandardDataset, and legacy metric-class tasks to the sibling datasets-and-metrics sub-skill.
- Route legacy
aif360.algorithms preprocessing/inprocessing/postprocessing class workflows to the sibling mitigation-algorithms sub-skill.
- Route MDSS subgroup scans, FACTS counterfactual subgroup rules, and metric text/JSON explainers to the sibling
detectors-and-explainers sub-skill. This sub-skill only covers aif360.sklearn.metrics.mdss_bias_score as a metric helper.
- Do not rely on source notebooks, tests, or external raw-data locations at runtime. Use the bundled references and scripts here, plus user-provided data or cache settings.
Safety and verification status
- Base CPU imports and in-memory sklearn metric workflows are supported by this sub-skill.
- Dataset fetchers can require network access or a warmed cache; do not use them in no-network smoke checks.
- Optional extras were not installed in the verified base environment. Treat extra-gated estimators and
ot_distance as optional/unverified until the matching extra is installed and checked.