利用python畫出AUC曲線的例項
阿新 • • 發佈:2020-02-28
以load_breast_cancer資料集為例,模型細節不重要,重點是畫AUC的程式碼。
直接上程式碼:
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] #############畫圖部分 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,'b',label = 'Val AUC = %0.3f' % roc_auc) plt.legend(loc = 'lower right') plt.plot([0,1],[0,'r--') plt.xlim([0,1]) plt.ylim([0,1]) plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') plt.show()
補充拓展:Python機器學習中的roc_auc曲線繪製
廢話不多說,直接上程式碼
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,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,threshold=roc_curve(y_test,y_score[:,1]) roc_auc=auc(fpr,tpr) plt.figure(figsize=(10,10)) plt.plot(fpr,color='darkorange',lw=2,label='ROC curve (area = %0.2f)' % roc_auc) ###假正率為橫座標,真正率為縱座標做曲線 plt.plot([0,color='navy',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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