random forest machine learning

auc_roc.py

fpr_dt, tpr_dt, thresholds_lstm = roc_curve(y_test, y_test_pred_dt[:,1])
fpr_rf, tpr_rf, thresholds_lstm = roc_curve(y_test, y_test_pred_rf[:,1])

dt_auc = roc_auc_score(y_test, y_test_pred_dt[:,1])
rf_auc = roc_auc_score(y_test, y_test_pred_rf[:,1])

plt.figure(figsize=(10,8))

plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr_dt, tpr_dt, label='Decision Tree (AUC = {:.3f})'.format(dt_auc))
plt.plot(fpr_rf, tpr_rf, label='Random Forest (AUC = {:.3f})'.format(rf_auc))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
plt.show()