如何得到决策树的ROC曲线？

我正在努力寻找ROC曲线 and AUROC曲线对于决策树。我的代码是这样的

clf.fit(x,y)
y_score = clf.fit(x,y).decision_function(test[col])
pred = clf.predict_proba(test[col])
print(sklearn.metrics.roc_auc_score(actual,y_score))
fpr,tpr,thre = sklearn.metrics.roc_curve(actual,y_score)

output:

 Error()
'DecisionTreeClassifier' object has no attribute 'decision_function'

基本上，在查找时会出现错误y_score。请解释一下是什么y_score以及如何解决这个问题？

首先，DecisionTreeClassifier has no属性decision_function.

如果我从你的代码结构猜测，你看到了这个example http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

在这种情况下，分类器不是决策树，而是支持 Decision_function 方法的 OneVsRestClassifier。

您可以看到可用的属性DecisionTreeClassifier here http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

一种可能的方法是二值化类进而计算每个类别的 auc：

Example:

from sklearn import datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.tree import DecisionTreeClassifier
from scipy import interp


iris = datasets.load_iris()
X = iris.data
y = iris.target

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 = DecisionTreeClassifier()

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

fpr = dict()
tpr = dict()
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])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

#ROC curve for a specific class here for the class 2
roc_auc[2]

Result

0.94852941176470573