Python机器学习中的roc_auc曲线绘制

from sklearn.metrics  import roc_curve,auc

from  sklearn.ensemble import RandomForestClassifier

import matplotlib.pyplot  as plt

from sklearn.model_selection import train_test_split

x_train,y_train,x_test,y_test=train_test_split(x,y,test_size=0.2)

rf=RandomForestClassifier()

rf.fit（x_train，y_train）

rf.score(x_train,y_train)

print('trainscore:'+str(rfbest.score(x_train,y_train)))
print('testscore:'+str(rfbest.score(x_test,y_test)))

y_score=rfbest.fit(x_train,y_train).predict_proba(x_test) #descision_function()不可用

print(type(y_score))

fpr,tpr,threshold=roc_curve(y_test,y_score[:, 1])
roc_auc=auc(fpr,tpr)
plt.figure(figsize=(10,10))
plt.plot(fpr, tpr, color='darkorange',
lw=2, label='ROC curve (area = %0.2f)' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()