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All the theory references for policytree are for adaptive trees, but some users may want to construct "honest" trees by fitting in one sample, then repopulating with the argmax in a new sample, like tree.new <- repopulate_leaves(tree.old. X, Y). This is fairly straight forward to implement in R (predict region with type="node.id", select argmax, then replace action ids in the internal tree data structure stored in the R object. We would not take pruning into account here though).
Leaving as an issue for now, awaiting feedback on potential interest, as @halflearned already has a separate Python solution working for his use case.
The text was updated successfully, but these errors were encountered:
All the theory references for policytree are for adaptive trees, but some users may want to construct "honest" trees by fitting in one sample, then repopulating with the argmax in a new sample, like
tree.new <- repopulate_leaves(tree.old. X, Y)
. This is fairly straight forward to implement in R (predict region withtype="node.id"
, select argmax, then replace action ids in the internal tree data structure stored in the R object. We would not take pruning into account here though).Leaving as an issue for now, awaiting feedback on potential interest, as @halflearned already has a separate Python solution working for his use case.
The text was updated successfully, but these errors were encountered: