I was working with a dataset that had a textual column as well as numerical columns, so I used
TFIDF for the textual column and created a sparse matrix, similarly for the numerical features I created a sparse matrix using
scipy.sparse.csr_matrix and combined them with the text sparse features.
Then I'm feeding the algorithm to a gradient boosting model and doing the rest of the training and prediction. However I want to know, is there any way I can plot the feature importance, of this sparse matrix and will be able to know the important feature column names?