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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")

在许多关于缺少可执行文件的错误之后，现在程序已成功完成。该文件已创建，但它是空的。我究竟做错了什么？

4

1 回答 1

0

这是一个对我有用的输出示例，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")
于 2016-10-03T06:43:17.510 回答