如何在python中解释sklearn的descision树结果

Question

1. 环境： win10，python3，sklearn-0.18.2
2. 问题描述： 我使用一个变量'province_area'来预测一个人的好坏。考虑变量'province_area'是分类特征，所以使用'DictVectorizer''fit_transform'来处理它。但是在生成树之后，标签“'province_area = **'＆lt; = 0.5”中有一个小红色方块。请点击链接获取图表。你能帮我告诉我如何知道红场的实际价值吗？

以下代码

from pandas import Series,DataFrame
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
dict_X_train = x.to_dict(orient='records')
x_train =vec.fit_transform(dict_X_train).toarray()
X_train, X_test, y_train, y_test = train_test_split(x_train, y, test_size = 0.3, random_state = 100)
clf = clf.fit(X_train, y_train)

import pydotplus
from IPython.display import Image  

dot_data = tree.export_graphviz(clf, out_file=None,
                     feature_names=vec.get_feature_names(),  
                     filled=True, rounded=True, proportion=True,   
                     special_characters=False)  
graph = pydotplus.graph_from_dot_data(dot_data)  
Image(graph.create_png())

the decision tree graph