![]() ![]() However is there any way to print the decision-tree based on GridSearchCV. One solution is taking the best parameters from gridsearchCV and then form a decision tree with those parameters and plot the tree. As the name suggests, in Decision Tree, we form a tree-like model of decisions and. Don’t forget to include the featurenames parameter, which indicates the feature names, that will be used when displaying the tree. That is why it is also known as CART or Classification and Regression Trees. The tree.dot file will be saved in the same directory as your Jupyter Notebook script. #use gridsearch to test all values for n_neighborsĭtc_gscv = gsc(dtc, parameter_grid, cv=5,scoring='accuracy',n_jobs=-1) dot file, which is the standard extension for graphviz files. > 395 if len(self.feature_names) != decision_tree.n_features_:ģ96 raise ValueError("Length of feature_names, %d "ģ97 "does not match number of features, %d"ĪttributeError: 'GridSearchCV' object has no attribute 'n_features_'Ĭode for decision-tree based on GridSearchCV dtc=DecisionTreeClassifier() usr/local/lib/python3.6/dist-packages/sklearn/tree/_export.py in export(self, decision_tree) #plotting : decision tree on information/entropy basedĭot_data = export_graphviz(dtc_entropy, out_file=None, filled=True, rounded=True,Ĭode #plotting : decision tree with GRIDSEARCHCV (dtc_gscv) on information/entropy basedĭot_data = export_graphviz(dtc_gscv, out_file=None, filled=True, rounded=True,Įrror -ĪttributeError Traceback (most recent call last)Ħ dot_data = export_graphviz(dtc_gscv, out_file=None, filled=True, rounded=True, A dot file is a Graphviz representation of a decision tree. need to run conda install graphviz and conda install python-graphviz at the. The first part of this process involves creating a dot file. Think about what would happen if we grew the decision tree in Figure 5.1. I should note that the reason why I am going over Graphviz after covering Matplotlib is that getting this to work can be difficult. However if i try to plot a normal decision tree without GridSearchCv, then it successfully prints.Ĭode # dtc_entropy : decison tree classifier based on entropy/information Gain In data science, one use of Graphviz is to visualize decision trees. AttributeError: 'GridSearchCV' object has no attribute 'n_features_' ![]() I was trying to plot the decision tree which is formed with GridSearchCV, but its giving me an Attribute error.
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