At each node of a decision tree, we must choose a collection of features to split along.
Suppose we know a priori that the features can be partitioned into subsets that are 'correlated', i.e. this partition describes someone's hat and this partition describes their shoes
Is there anyway to force this partitioning to be used when choosing which features to split along?
Like if you are choosing $k$ features, make sure that all $k$ are from the same partition.