# Calculate confusion matrix values
TN = ((y_true == 0) & (y_pred == 0)).sum()
FP = ((y_true == 0) & (y_pred == 1)).sum()
FN = ((y_true == 1) & (y_pred == 0)).sum()
TP = ((y_true == 1) & (y_pred == 1)).sum()

sensitivity = recall
specificity = TN / (TN + FP) if (TN + FP) != 0 else 0
npv = TN / (TN + FN) if (TN + FN) != 0 else 0
TPR = recall
FPR = FP / (FP + TN)
FNR = FN / (TP + FN)
TNR = specificity
ACC = accuracy
F1S = f1
MCC = mcc

print("{} - Sensitivity = {:.2f}%".format(model_name, sensitivity * 100))
print("{} - Specificity = {:.2f}%".format(model_name, specificity * 100))
print("{} - Precision   = {:.2f}%".format(model_name, precision * 100))
print("{} - NPV         = {:.2f}%".format(model_name, npv * 100))
print("{} - TPR         = {:.2f}%".format(model_name, TPR * 100))
print("{} - FPR         = {:.2f}%".format(model_name, FPR * 100))
print("{} - FNR         = {:.2f}%".format(model_name, FNR * 100))
print("{} - TNR         = {:.2f}%".format(model_name, TNR * 100))
print("{} - ACC         = {:.2f}".format(model_name, ACC))
print("{} - F1 Score    = {:.2f}%".format(model_name, F1S * 100))
print("{} - MCC         = {:.2f}".format(model_name, MCC))
print("{} - ROC AUC     = {:.2f}".format(model_name, roc_auc))

return {'Model': model_name, 'Sensitivity': sensitivity, 'Specificity': specificity, 
        'Precision': precision, 'NPV': npv, 'TPR': TPR, 'FPR': FPR, 'FNR': FNR, 
        'TNR': TNR, 'ACC': ACC, 'F1 Score': F1S, 'MCC': MCC, 'ROC AUC': roc_auc}