python为什么叫爬虫，python多分类_python中多类分类的ROC或CAP曲线

要绘制多类ROC，请在类上使用label_binarize函数。在

使用虹膜数据的示例：import matplotlib.pyplot as plt

from sklearn import svm, datasets

python为什么叫爬虫。from sklearn.model_selection import train_test_split

from sklearn.preprocessing import label_binarize

from sklearn.metrics import roc_curve, auc

from sklearn.multiclass import OneVsRestClassifier

python roc。iris = datasets.load_iris()

X = iris.data

y = iris.target

# Binarize the output

python 类 self。y = label_binarize(y, classes=[0, 1, 2])

n_classes = y.shape[1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)

classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,

python代码？random_state=0))

y_score = classifier.fit(X_train, y_train).decision_function(X_test)

fpr = dict()

tpr = dict()

python界面，roc_auc = dict()

for i in range(n_classes):

fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])

roc_auc[i] = auc(fpr[i], tpr[i])

python和java？colors = cycle(['blue', 'red', 'green'])

for i, color in zip(range(n_classes), colors):

plt.plot(fpr[i], tpr[i], color=color, lw=lw,

label='ROC curve of class {0} (area = {1:0.2f})'

python版本，''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k ', lw=lw)

plt.xlim([-0.05, 1.0])

plt.ylim([0.0, 1.05])

python 类？plt.xlabel('False Positive Rate')

plt.ylabel('True Positive Rate')

plt.title('Receiver operating characteristic for multi-class data')

plt.legend(loc="lower right")

python 创建类，plt.show()