Question

我想计算没有二元分类器的auc roc分数，我似乎没有在scikit-learn中找到一个例子。知道怎么做吗？

感谢

Answer 1

多类auc roc可以通过独立计算每个类的曲线或聚合类之间的值来实现。

您可以找到更多信息here，但在下面我还附上了绘制各种曲线的代码（代码来自附带的链接）：

# Compute macro-average ROC curve and ROC area

# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
     label='micro-average ROC curve (area = {0:0.2f})'
           ''.format(roc_auc["micro"]),
     color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
     label='macro-average ROC curve (area = {0:0.2f})'
           ''.format(roc_auc["macro"]),
     color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
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})'
         ''.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')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()

计算没有二元分类器的auc roc（scikit-learn）

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