如何从 gridsearchcv 绘制决策树？

Posted 2023-03-12

【中文标题】如何从 gridsearchcv 绘制决策树？

【英文标题】：how to plot a decision tree from gridsearchcv?

【发布时间】：2020-08-19 21:02:20

【问题描述】：

我试图绘制由 GridSearchCV 形成的决策树，但它给了我一个属性错误。

AttributeError: 'GridSearchCV' object has no attribute 'n_features_'

但是，如果我尝试在没有 GridSearchCv 的情况下绘制正常的决策树，那么它会成功打印。

代码[没有 gridsearchcv 的决策树]

# dtc_entropy : decison tree classifier based on entropy/information Gain
#plotting : decision tree on information/entropy  based

from sklearn.tree import export_graphviz
import graphviz

feature_names = x.columns

dot_data = export_graphviz(dtc_entropy, out_file=None, filled=True, rounded=True,
                                feature_names=feature_names,  
                                class_names=['0','1','2'])
graph = graphviz.Source(dot_data)  
graph                           ### --------------> WORKS 

代码 [带有 gridsearchcv 的决策树]

#plotting : decision tree with GRIDSEARCHCV (dtc_gscv)  on information/entropy  based
from sklearn.tree import export_graphviz
import graphviz

feature_names = x.columns

dot_data = export_graphviz(dtc_gscv, out_file=None, filled=True, rounded=True,
                                feature_names=feature_names,  
                                class_names=['0','1','2'])
graph = graphviz.Source(dot_data)  
graph                            ##### ------------> ERROR

错误

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-201-603524707f02> in <module>()
      6 dot_data = export_graphviz(dtc_gscv, out_file=None, filled=True, rounded=True,
      7                                 feature_names=feature_names,
----> 8                                 class_names=['0','1','2'])
      9 graph = graphviz.Source(dot_data)
     10 graph

1 frames
/usr/local/lib/python3.6/dist-packages/sklearn/tree/_export.py in export(self, decision_tree)
    393         # n_features_ in the decision_tree
    394         if self.feature_names is not None:
--> 395             if len(self.feature_names) != decision_tree.n_features_:
    396                 raise ValueError("Length of feature_names, %d "
    397                                  "does not match number of features, %d"

AttributeError: 'GridSearchCV' object has no attribute 'n_features_'

基于 GridSearchCV 的决策树代码

dtc=DecisionTreeClassifier()

#use gridsearch to test all values for n_neighbors
dtc_gscv = gsc(dtc, parameter_grid, cv=5,scoring='accuracy',n_jobs=-1)

#fit model to data
dtc_gscv.fit(x_train,y_train)

一个解决方案是从 gridsearchCV 中获取最佳参数，然后用这些参数形成决策树并绘制树。

但是有什么方法可以打印基于 GridSearchCV 的决策树。

【参考方案1】：

你可以试试：

dot_data = export_graphviz(dtc_gscv.best_estimator_, out_file=None, 
            filled=True, rounded=True, feature_names=feature_names, class_names=['0','1','2'])

【讨论】：

@MAC，应该。 best_estmiator_ 用于分类和回归

但是类名应该是可选的。我正在尝试这个。 dot_data = tree.export_graphviz(model.best_estimator_.fit(X_train,y_train) , out_file=None, filled=True, rounded=True, feature_names=X_train.columns) graph = graphviz.Source(dot_data) graph

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