问题描述
我正在尝试为决策树找到 ROC曲线和 AUROC曲线。我的代码是这样的
I am trying to find ROC curve and AUROC curve for decision tree. My code was something like
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)
输出：
Error()
'DecisionTreeClassifier' object has no attribute 'decision_function'
基本上，发现 y_score 时出现错误。请解释什么是 y_score 以及如何解决此问题？
basically, the error is coming up while finding the y_score. Please explain what is y_score and how to solve this problem?
推荐答案
首先， DecisionTreeClassifier 没有属性 decision_function 。
如果从您的代码结构中猜出，您会看到
If I guess from the structure of your code , you saw this example
在这种情况下，分类器不是决策树，而是支持Decision_function方法的OneVsRestClassifier。
In this case the classifier is not the decision tree but it is the OneVsRestClassifier that supports the decision_function method.
您可以看到 DecisionTreeClassifier
一种可行的方法是二进制化类，然后计算每个类的auc：
A possible way to do it is to binarize the classes and then compute the auc for each class:
示例：
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]
结果
0.94852941176470573
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