labels_7.py

# Performance Metrics
y_pred_rf = rf.predict_proba(X_test)[:, 1]
y_pred = rf.predict(X_test)
fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_rf)
print(classification_report(y_test, y_pred))

print("Confusion Matrix")
print(confusion_matrix(y_test, y_pred))

print('')
print("Accuracy")
print(accuracy_score(y_test, y_pred))

#plotting ROC curve
plt.figure(1)
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr_rf, tpr_rf, label='RF')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
plt.show()