def tree_to_code(tree, feature_names):
tree_ = tree.estimators_
#print(tree_)
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print("def tree({}):".format(", ".join(feature_names)))
symptoms_present = []
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print(name + " ?")
ans = input()
ans = ans.lower()
if ans == 'yes':
val = 1
else:
val = 0
if val <= threshold:
recurse(tree_.children_left[node], depth + 1)
else:
symptoms_present.append(name)
recurse(tree_.children_right[node], depth + 1)
else:
present_disease = print_disease(tree_.value[node])
print( "You may have " + present_disease )
red_cols = reduced_data.columns
symptoms_given = red_cols[reduced_data.loc[present_disease].values[0].nonzero()]
print("symptoms present " + str(list(symptoms_present)))
print("symptoms given " + str(list(symptoms_given)) )
confidence_level = (1.0*len(symptoms_present))/len(symptoms_given)
print("confidence level is " + str(confidence_level))
recurse(0, 1)
tree_to_code(clf,cols)
在将决策树代码更改为随机森林代码时，在第6行出现属性错误：对于tree_.feature中的i。请帮助我们解决错误。它可以使用决策树分类器，但不能使用随机森林分类器。