Fairness, robustness, and data quality audits for traditional ML models
Project description
rai-audit-ml
Fairness, robustness, data quality, and production drift audits for tabular ML models.
from rai_audit.ml import ClassificationAudit, DriftAudit, RegressionAudit
DriftAudit compares a reference window with a current batch. It checks numeric
feature distributions, prediction distributions, sensitive-feature subgroup
composition, and classification error-rate changes per sensitive group.
See examples/ml_drift_monitoring/batch_monitor.py and
examples/mlops_integrations/ in the repository for monitoring examples.
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