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?
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.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
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.
