[英]Is there any way to visualize decision tree (sklearn) with categorical features consolidated from one hot encoded features?
[英]how to pass mixed (categorical and numeric) features to Decision Tree Regressor in sklearn?
如何将分类和数字特征传递给 sklearn 中的 DecisionTreeRegressor?
下面的代码显示了如何将DecisionTreeRegressor
用于数字特征:
from sklearn import tree
make_tree = tree.DecisionTreeRegressor()
fit_tree = make_tree.fit(X_train, y_train)
首先,所有分类特征都应该被编码(用数字表示)以便回归模型可以解释。 为此,您可以使用LabelEncoder后跟OneHotEncoder 。 对于高基数特征,可以使用FeatureHasher 。
举个例子:
from sklearn.feature_extraction import FeatureHasher
# n_feature: number of unique values in the feature(s)
# input_type should be passed as 'string' to be compatible to pandas DataFrames
feature_hasher = FeatureHasher(n_features=5000, input_type='string')
df['COLUMN_NAME'] = feature_hasher.transform(df['COLUMN_NAME'])
然后,您可以将您的特征传递给回归器。
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