I am using SKLearn XGBoost model for my binary classification problem. My data contains nominal categorical features (such as race) for which one hot encoding should be used to feed them to the tree based models.
On the other hand, using feature_importances_ variable of XGBoost yields us the importance of each column on the trained model. So if I do the encoding and then get the features importance of columns, the result will includes names like race_2 and its importance.
What should I do to solve this problem and get a whole score for each nominal feature? Can I take the average of one hot encoded columns importance scores that belong to one feature? (like race_1, race_2 and race_3)
First of all, if your goal is to select the most useful features for later training, I would advise you to use regularization in your model. In the case of xgboost, you can tune the parameter gamma so the model would actually be more dependent on "more useful" features (i.e. tune the minimum loss reduction required for the model to add a partition leaf). Here is a good article on implementing regularization into xgboost models.
On the other hand, if you insist on doing feature importance, I would say grouping the encoded variables and simply adding them is not a good decision. This would result in feature-importance results that do not consider the relationship between these dummy variables.
My suggestion would be to take a look at the permutation tools for this. The basic idea is you take your original dataset, shuffle the values on the column in which you are going to calculate feature importance, train the model and record the score. Repeat this over different columns and the effect of each on the model performance would be a sign of their importance. It is actually easier done than said, sklearn has this feature built-in to do for you: check out the example provided in here.