# Listing 16
probs = clf.predict_proba(x5)
prob=probs[:,1]

fpr, tpr, threshold = metrics.roc_curve(y5, prob)
roc_auc = metrics.auc(fpr, tpr)

plt.plot(fpr, tpr, 'b', label = "AUC = %0.2f" % roc_auc)
plt.legend(loc = "lower right")
plt.plot([0,1],[0,1], 'r--')
plt.xlabel("False Positive Rate" , fontsize=12)
plt.ylabel("True Positive Rate" , fontsize=12)

#plt.savefig('full_overlapped_data_set_roc.png', dpi=300)
plt.show()