Extracting the best fitted DecisionTreeClassifier after Grid Search

@syed.danish wrote:

I have implemented grid search to find the best decision tree that could be fitted to my training data using the following code :

parameters={'min_samples_split' : range(10,500,20),'max_depth': range(1,20,2)}
clf_tree=tree.DecisionTreeClassifier()
clf=grid_search.GridSearchCV(clf_tree,parameters)
clf.fit(features,labels)

After the grid search the best parameters were :
{'max_depth': 17, 'min_samples_split': 30}

Now I want to print the tree that was finally fitted to the training data set using the function :
def printTree(clf_tree):
---from sklearn import tree
---tree.export_graphviz(clf_tree,out_file='tree.dot')
---from sklearn.externals.six import StringIO
---import pydot
---dot_data = StringIO()
---tree.export_graphviz(clf_tree, out_file=dot_data)
---graph = pydot.graph_from_dot_data(dot_data.getvalue())
---a=graph.write_png("tree.png")
---from IPython.display import Image
---import os
---return Image(filename=os.getcwd()+'/tree.png')

The input required for the function is the decision tree object, Is there any way to extract the best fitted DecisionTreeClassifier from it? I am aware of creating a new decision tree object with the best parameters, so please suggest a way other than this.

Thanks in advance

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