本文整理汇总了Python中sklearn.tree.DecisionTreeClassifier.export_graphviz方法的典型用法代码示例。如果您正苦于以下问题：Python DecisionTreeClassifier.export_graphviz方法的具体用法？Python DecisionTreeClassifier.export_graphviz怎么用？Python DecisionTreeClassifier.export_graphviz使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.tree.DecisionTreeClassifier的用法示例。
在下文中一共展示了DecisionTreeClassifier.export_graphviz方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train_test_split
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import export_graphviz [as 别名]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=7)
#
# TODO: Create an DT classifier. No need to specify any parameters
#
dt = DecisionTreeClassifier()
#
# TODO: train the classifier on the training data / labels:
# TODO: score the classifier on the testing data / labels:
#
dt.fit(X_train, y_train)
score = dt.score(X_test, y_test)
print "High-Dimensionality Score: ", round((score*100), 3)
#
# TODO: Use the code on the courses SciKit-Learn page to output a .DOT file
# Then render the .DOT to .PNGs. Ensure you have graphviz installed.
# If not, `brew install graphviz`.
#
dt.export_graphviz(dt.tree_, out_file='tree.dot', feature_names=X.columns)
from subprocess import call
call(['dot', '-T', 'png', 'tree.dot', '-o', 'tree.png'])