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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)

output:

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

basically, the error is coming up while finding the y_score. Please explain what is y_score and how to solve this problem?

RobJan
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kanav anand
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2 Answers2

7

First of all, the DecisionTreeClassifier has no attribute decision_function.

If I guess from the structure of your code , you saw this example

In this case the classifier is not the decision tree but it is the OneVsRestClassifier that supports the decision_function method.

You can see the available attributes of DecisionTreeClassifier here

A possible way to do it is to binarize the classes and then compute the auc for each class:

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
Community
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seralouk
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4

Think that for a decision tree you can use .predict_proba() instead of .decision_function() so you will get something as below:

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

Then, the rest of the code will be the same. In fact, the roc_curve function from scikit learn can take two types of input: "Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers)." See here for more details.

Liky
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    You can use the positive class y_score = tree_model.predict_proba(X_test)[:, 1], or merely the prediction y_score = tree_model.predict(X_test) – Freeman Nov 10 '18 at 11:26