AUC with sklearn vary each time script is started I'm using the following code to perform a tree classification. I set up an int value for random_state in train_test_split function but each time I got different values for auc or accuracy_score. 
I don't see what I am missing...

X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state=1,stratify=Y, test_size=0.33)

clf = clf.fit(X_train, y_train)

predicted_probas = clf.predict_proba(X_test)
y_predict = clf.predict(X_test)

print(accuracy_score(y_test, y_predict))

print(classification_report(y_test, y_predict))

classes = np.unique(y_test)
probas = predicted_probas

fpr = {}
tpr = {}
roc_auc = {}

for i in range(len(classes)):
        fpr[i], tpr[i], _ = roc_curve(y_test, probas[:,i],pos_label=classes[i])
        roc_auc[i] = auc(fpr[i], tpr[i])
        print(classes[i])
        print(fpr[i], tpr[i])
        print("roc_auc")
        print(roc_auc[i])
Ok  I miss the fact that you can set also random_state here to remove this variability... but I don't see how it works here exactly so if someone want to explain...your are welcome.

   clf = tree.DecisionTreeClassifier(random_state=5)