利用python画出AUC曲线的实例
以load_breast_cancer数据集为例,模型细节不重要,重点是画AUC的代码。
直接上代码:
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()
补充拓展:Python机器学习中的roc_auc曲线绘制
废话不多说,直接上代码
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()
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