Decision Tree - Preprocessing for very sparse features How do we pre-process data for very sparse features for a decision tree?

From this Turi documentation for decision trees

It mentioned this:


  Why chose decision trees? Different kinds of models have different
  advantages. The decision tree model is very good at handling tabular
  data with numerical features, or categorical features with fewer than
  hundreds of categories. Unlike linear models, decision trees are able
  to capture non-linear interaction between the features and the target.
  
  One important note is that tree based models are not designed to work
  with very sparse features. When dealing with sparse input data (e.g.
  categorical features with large dimension), we can either pre-process
  the sparse features to generate numerical statistics, or switch to a
  linear model, which is better suited for such scenarios.


If we have a categorical column that is very sparse (many 0's), then for that category what type of numerical statistics can we transform for each row?
There are many feature engineering options for sparse categorical data. A couple of common options:

Remove features - Drop columns that are sparse
Feature reduction - One example is singular value decomposition (SVD)
Reduce number of categories - Using domain knowledge, group together related low frequency categories
Use sparse representation - only stores nonzero elements