clusterDF=pd.DataFrame(data=clusterdata[:,:],index=list(range(len(clusterdata))),
    columns=['viewed','carted','knownpurchases','totlength','avgtime','stdtime','vartime','KMP','Leven','prodnum','Class'])

X = clusterDF.drop('Class', axis=1)
y = clusterDF['Class']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
regressor = DecisionTreeRegressor(max_depth=2)
regressor.fit(X_train, y_train)

y_pred = regressor.predict(X_test)

df=pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})

#Problematic line
export_graphviz(regressor, out_file='foo.dot', feature_names=['carted'])