from scipy.sparse import hstack

    X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe, 
                    X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, 

    X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, 
                    X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, 

X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.

Now, I applied a decision tree classifier on this model and got this:

enter image description here

I took max_depth as 3 just for visualization purposes.

My question is: I would like to get feature names in my output instead of index as X2599, X4 etc. I know I can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since I already merged this vectorized data using hstack. Now how do you get feature names in this decision tree?

  • $\begingroup$ It would be helpful to include your visualization code. $\endgroup$ Commented May 20, 2023 at 23:01

3 Answers 3


If you plot with sklearn.tree.plot_tree, there is a parameter for feature_names:

feature_names: list of strings, default=None Names of each of the features. If None, generic names will be used (“X[0]”, “X[1]”, …).


hstack preserves the order of the columns, so you can piece together the feature names for each of your component arrays. OneHotEncoder (if that's what you used) and CountVectorizer both support get_feature_names, so concatenating the lists of feature names should be possible. To give full details would require more details about how each of the arrays was generated. You might consider using ColumnTransformer in the future, which handles all that concatenation for you and also provides its own get_feature_names.

  • $\begingroup$ (In newer versions of sklearn, all preprocessors provide this method, but it has been renamed get_feature_names_out.) $\endgroup$
    – Ben Reiniger
    Commented May 2 at 12:28

You can use graphviz instead. and use the following code to view the decision tree with feature names.

import pydotplus
import sklearn.tree as tree
from IPython.display import Image

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
    feature_names=dt_feature_names, class_names=dt_target_names,
graph = pydotplus.graph_from_dot_file('tree.dot')

This will display feature names with values, gini coefficient, sample, value and class

  • $\begingroup$ This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features $\endgroup$
    – torBhakt
    Commented Mar 24, 2019 at 10:57

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