决策树python sklearn 示例

本文主要是使用python sklearn，完成决策树的demo,以及可视化，最终生成的决策树结果。

from sklearn.datasets import load_irisfrom sklearn import treefrom sklearn.tree import export_graphvizimport subprocessdef visualize_tree(tree, feature_name, dot_file):    """Create tree png using graphviz.    tree -- scikit-learn DecsisionTree.    feature_names -- list of feature names.    dot_file -- dot file name and path    """    with open("tree.dot", 'w') as f:        export_graphviz(tree, out_file=f,                        feature_names=feature_name)    dt_png = "dt.png"    command = ["dot", "-Tpng", dot_file, "-o", dt_png]    try:        subprocess.check_call(command)    except Exception as e:        print e        exit("Could not run dot, ie graphviz, to "             "produce visualization")def iris_demo():    clf = tree.DecisionTreeClassifier()    iris = load_iris()    # iris.data属性150*4,iris.target 类别归一化为了0,1,2(150*1)    clf = clf.fit(iris.data, iris.target)    dot_file = 'tree.dot'    tree.export_graphviz(clf, out_file=dot_file)    visualize_tree(clf, iris.feature_names, dot_file)    # (graph,) = pydot.graph_from_dot_file('tree.dot')    # graph.write_png('somefile.png')if __name__ == '__main__':    iris_demo()    pass

数据集

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1. 花的分类的四种属性，150个示例

2. 花的分类，一共三类对应于0,1,2

3. 花的四个属性的描述

最终生成的结果：

pydot的安装见另一篇bolg

http://blog.csdn.net/haluoluo211/article/details/78200078

转载注明出处，并在下面留言！！！

参考

http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html

http://www.kdnuggets.com/2017/05/simplifying-decision-tree-interpretation-decision-rules-python.html