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Fairness Mitigation Evaluation on German Credit (test)

-0.1068DPDLR
-0.111496-0.079798-0.0481-0.016402Apr 8, 2026

Evaluation results

MethodLinks
LRBase Model=Logistic Regression, Intervention=None2026.04-0.1068-0.12280.0806
XGBBase Model=XGBoost, Intervention=None2026.04-0.0704-0.08430.0699
RFBase Model=Random Forest, Intervention=None2026.04-0.0599-0.07190.0603
LR + CAFPBase Model=Logistic Regression, Intervention=CAFP2026.04-0.0505-0.06470.0713
RF + CAFPBase Model=Random Forest, Intervention=CAFP2026.04-0.043-0.05440.0577
XGB + CAFPBase Model=XGBoost, Intervention=CAFP2026.04-0.0407-0.05280.0611
XGB + Eq.OddsBase Model=XGBoost, Intervention=Equalized Odds2026.04-0.0345-0.04940.0751
LR + Eq.OddsBase Model=Logistic Regression, Intervention=Equalized Odds2026.04-0.0289-0.04520.0824
RF + Eq.OddsBase Model=Random Forest, Intervention=Equalized Odds2026.04-0.0217-0.03640.074
XGB + R. O.Base Model=XGBoost, Intervention=Reject Option2026.04-0.0015-0.02020.0938
LR + R. O.Base Model=Logistic Regression, Intervention=Reject Option2026.040.0055-0.01590.1077
RF + R. O.Base Model=Random Forest, Intervention=Reject Option2026.040.0106-0.00830.0955