python中二元分类的ROC曲线 [英] ROC curve for binary classification in python

问题描述

我想使用 RandomForestClassifier

我有两个 numpy 数组，一个包含预测值，一个包含真实值，如下所示:

I have two numpy arrays one contains predicted values and one contains true values as follows:

In [84]: test
Out[84]: array([0, 1, 0, ..., 0, 1, 0])

In [85]: pred
Out[85]: array([0, 1, 0, ..., 1, 0, 0])

如何在 ipython 中移植 ROC 曲线并获得此二元分类结果的 AUC(曲线下面积)?

How do I port ROC curve and obtain AUC (Area Under Curve) for this binary classification result in ipython?

推荐答案

您需要概率来创建 ROC 曲线.

You need probabilities to create ROC curve.

In [84]: test
Out[84]: array([0, 1, 0, ..., 0, 1, 0])

In [85]: pred
Out[85]: array([0.1, 1, 0.3, ..., 0.6, 0.85, 0.2])

来自 scikit-learn 示例的示例代码:

Example code from scikit-learn examples:

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(2):
    fpr[i], tpr[i], _ = roc_curve(test, pred)
    roc_auc[i] = auc(fpr[i], tpr[i])

print roc_auc_score(test, pred)
plt.figure()
plt.plot(fpr[1], tpr[1])
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')
plt.show()

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