What would be the correct way to get the roc_auc_score for my encrypted model please?
lg_enc = ConcreteLogisticRegression(solver='lbfgs', C= 3.98, class_weight = 'balanced', penalty= 'l2', n_bits=8, random_state=15) # fit the model lg_enc.fit(X_train, y_train) # compile and predict circuit = lg_enc.compile(X_train) y_pred_enc = lg_enc.predict(X_test, fhe="execute")
If it was a plain model, I would use:
# calculate precision-recall AUC roc_auc_score(y_test, lg.predict_proba(X_test), average='macro', multi_class='ovr')
as per the documentation:
how can I do the same with my encrypted model?
is it just the same?
roc_auc_score(y_test, lg_enc.predict_proba(X_test), average='macro', multi_class='ovr')