利用python画出AUC曲线的实例

from sklearn.datasets import load_breast_cancer
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pylab as plt
import warnings;warnings.filterwarnings(\'ignore\')
dataset = load_breast_cancer()
data = dataset.data
target = dataset.target
X_train,X_test,y_train,y_test = train_test_split(data,target,test_size=0.2)
rf = RandomForestClassifier(n_estimators=5)
rf.fit(X_train,y_train)
pred = rf.predict_proba(X_test)[:,1]
#############画图部分
fpr, tpr, threshold = metrics.roc_curve(y_test, pred)
roc_auc = metrics.auc(fpr, tpr)
plt.figure(figsize=(6,6))
plt.title(\'Validation ROC\')
plt.plot(fpr, tpr, \'b\', label = \'Val AUC = %0.3f\' % roc_auc)
plt.legend(loc = \'lower right\')
plt.plot([0, 1], [0, 1],\'r--\')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel(\'True Positive Rate\')
plt.xlabel(\'False Positive Rate\')
plt.show()

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()