python - How to get ROC curve for decision tree? -

i trying find roc curve , auroc curve decision tree. code 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, error coming while finding y_score. please explain y_score , how solve problem?

first of all, decisiontreeclassifier has no attribute decision_function.

if guess structure of code , saw example

in case classifier not decision tree onevsrestclassifier supports decision_function method.

you can see available attributes of decisiontreeclassifier here

a possible way binarize classes , compute auc each class:

example:

from sklearn.metrics import roc_curve, auc sklearn.model_selection import train_test_split sklearn.preprocessing import label_binarize sklearn.tree import decisiontreeclassifier 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() 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 , roc area fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])  #roc curve specific class here class 2 roc_auc[2] 

result

0.94852941176470573