如何为GradientBoostingClassifier导出树?

Question

此代码适用于decisiontreecclassifier

r = export_text(tree2, feature_names=fn)
print(r)

对于RandomForestClassifier

from sklearn.tree import export_text
print(export_text(tree3.estimators_[0], 
spacing=3, decimals=3,
feature_names=fn))

AttributeError                            Traceback (most recent call last)
~AppDataLocalTemp/ipykernel_1840/2106124489.py in <module>
1 from sklearn.tree import export_text
----> 2 r = export_text(tree4, feature_names=fn)
3 print(r)
~anacondaanaconda3libsite-packagessklearnutilsvalidation.py in inner_f(*args, **kwargs)
61             extra_args = len(args) - len(all_args)
62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
64 
65             # extra_args > 0
~anacondaanaconda3libsite-packagessklearntree_export.py in export_text(decision_tree, feature_names, max_depth, spacing, decimals, show_weights)
875     """
876     check_is_fitted(decision_tree)
--> 877     tree_ = decision_tree.tree_
878     if is_classifier(decision_tree):
879         class_names = decision_tree.classes_
AttributeError: 'GradientBoostingClassifier' object has no attribute 'tree_'

是否有一种方法来显示在GradientBoostingClassifier的export_tree ?

Answer 1

您可以查看GradientBoostingClassifier (GBC)的底层决策树，而不是GBC本身。

假设GBC模型为mdl

mdl = GradientBoostingClassifier(n_estimators=100, max_depth=5)

你可以选择一个树并查看它

from pydotplus import graph_from_dot_data
from sklearn.tree import export_graphviz
from IPython.display import Image
gbc_sub_tree = mdl.estimators_[10, 0]
graph_data = export_graphviz(gbc_sub_tree, out_file=None, rounded=True, proportion=False, impurity=False)
tree_graph = graph_from_dot_data(graph_data)
Image(tree_graph.create_png())