本文整理汇总了Python中sklearn.tree.DecisionTreeClassifier._feature_names方法的典型用法代码示例。如果您正苦于以下问题：Python DecisionTreeClassifier._feature_names方法的具体用法？Python DecisionTreeClassifier._feature_names怎么用？Python DecisionTreeClassifier._feature_names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._feature_names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: ml_get_zoo_tree
# 需要导入模块: from sklearn.tree import DecisionTreeClassifier [as 别名]
# 或者: from sklearn.tree.DecisionTreeClassifier import _feature_names [as 别名]
def ml_get_zoo_tree(train_size=0.75, max_depth=5, random_state=245245):
# Load the zoo data
dataset = pd.read_csv(os.path.join(os.path.dirname(__file__), "data", "zoo.csv"))
# Drop the animal names since this is not a good feature to split the data on
dataset = dataset.drop("animal_name", axis=1)
# Split the data into a training and a testing set
features = dataset.drop("class", axis=1)
targets = dataset["class"]
train_features, test_features, train_targets, test_targets = \
train_test_split(features, targets, train_size=train_size, random_state=random_state)
# Train the model
tree = DecisionTreeClassifier(criterion="entropy", max_depth=max_depth)
tree = tree.fit(train_features, train_targets)
# Add the feature names to the tree for use in predict function
tree._feature_names = features.columns
return tree