rocauc = dict()for i in range(n_classes): fpr[i], tpr[i], = roc_curve(y_test_bin[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) 计算微平均ROC曲线 fpr[“micro”], tpr[“micro”], _ = roc_curve(y_test_bin.ravel(), y_score.ravel())roc_auc[“micro”...
from sklearn.preprocessing import label_binarizefrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import roc_curve, aucfrom sklearn.metrics import plot_roc_curve 加载数据集 iris = datasets.load_iris()X = iris.datay = iris.targe...
(2)fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1]) 该函数传入的参数一定要注意是一列,否则会报错(数组类型错误)该函数则得到我们想要的roc曲线的横纵坐标数组. fpr,tpr,thresholds 分别为假正率、真正率和阈值。(应该是不同阈值下的真正率和假正率)。 roc_auc =auc(fpr, tpr) ...
tpr[i],color=colors[i],lw=2,label='ROC curve of class {0} (area = {1:0.2f})'''.format(i,roc_auc[i]))plt.plot([0,1],[0,1],color='black',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...
\\train') import numpy as np import matplotlib.pyplot as plt from itertools import cycle from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass ...
python sklearn 也可以绘制多分类 roc, 具体见Multiclass Receiver Operating Characteristic (ROC) - scikit-learn. 其他 ROC 与 PR-Curve 的比较6 样本比例变化时的 ROC 曲线性能变化情况 由于ROC 的横纵坐标分别表示 FPR 和 TPR,二者的分母完全隔开,从而使得 AUC of ROC 不受正负样本比例的影响(如上图所示)...
from sklearn.metricsimportroc_curve,auc from sklearn.model_selectionimporttrain_test_split from sklearn.preprocessingimportlabel_binarize from sklearn.multiclassimportOneVsRestClassifier from scipyimportinterp 第二步导入所需要数据,本文所使用的是最常见的iris数据,预测输出变量是种类species,包含三种种类。
在Python中,我们可以使用scikit-learn库来实现多类别分类的ROC。以下是使用scikit-learn库进行多类别分类ROC的示例代码: fromsklearn.metricsimportroc_curve,aucfromsklearn.preprocessingimportlabel_binarizefromsklearn.multiclassimportOneVsRestClassifierfromsklearn.model_selectionimporttrain_test_splitfromsklearn.svmimp...
from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp # Import some data to play with ...
[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') ...