How do I include feature names in the plot_tree function from the XGBoost library?

Question

I’ve been using the XGBoost library to develop a binary classification model. Having trained my model I am interested in visualizing the individual trees to better understand my models predictions.

To do this XGBoost provides a plot_tree function but it only shows the integer index of the feature. Here is an example of one of my trees:

How do I include the feature name in this image rather than feature index (f28)?

Asked By: FChm

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Source

Answer 1

The plot_tree function in xgboost has an argument fmap which is a path to a ‘feature map’ file; this contains a mapping of the feature index to feature name.

The documentation on the feature map file is sparse, but it is a tab-delimited file where the first column is the feature indices (starting from 0 and ending at the number of features), the second column the feature name and the final column an indicator showing the type of feature (q=quantitative feature, i=binary feature).

An example of a feature_map.txt file:

0    feature_name_0    q
1    feature_name_1    i
2    feature_name_2    q
…          …           …

With this tab-delimited file you can then plot your tree from your trained model instance:

import xgboost
model = xgboost.XGBClassifier()

# train the model
model.fit(X, y)

# plot the decision tree, providing path to feature map file

xgboost.plot_tree(model,  num_trees=0, fmap='feature_map.txt')

Using this function displays the plot:

Answered By: FChm

Answer 2

I found that the easiest way is to set the feature names on the model directly. Obviously, the order needs to be correct.

model = XGBClassifier()    
model.get_booster().feature_names = feature_names # As a list or tuple!

Afterwards, the feature names will appear with the plot_tree function.

Answered By: Bestname

How do I include feature names in the plot_tree function from the XGBoost library?

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