我想使用sklearn导出决策树。
首先，我训练了一个决策树分类器：
self._selected_classifier = tree.DecisionTreeClassifier()
self._selected_classifier.fit(train_dataframe, train_class)
self._column_names = list(train_dataframe.columns.values)
之后，我使用以下方法导出决策树：
def _create_graph_visualization(self):
decision_tree_classifier = self._selected_classifier
from sklearn.externals.six import StringIO
dot_data = StringIO()
tree.export_graphviz(decision_tree_classifier,
out_file=dot_data,
feature_names=self._column_names)
import pydotplus
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("decision_tree_output.pdf")
在许多关于丢失可执行文件的错误之后，现在程序成功完成了。文件已创建，但为空。我做错了什么？
下面是一个输出的例子，它适用于我，使用pydotplus：
from sklearn import tree
import pydotplus
import StringIO
# Define training and target set for the classifier
train = [[1,2,3],[2,5,1],[2,1,7]]
target = [10,20,30]
# Initialize Classifier. Random values are initialized with always the same random seed of value 0
# (allows reproducible results)
dectree = tree.DecisionTreeClassifier(random_state=0)
dectree.fit(train, target)
# Test classifier with other, unknown feature vector
test = [2,2,3]
predicted = dectree.predict(test)
dotfile = StringIO.StringIO()
tree.export_graphviz(dectree, out_file=dotfile)
graph=pydotplus.graph_from_dot_data(dotfile.getvalue())
graph.write_png("dtree.png")
graph.write_pdf("dtree.pdf")
