I think you can just implement a new estimator on top of the tree, by
using the apply function to get the leaf a sample ends up in.
Then you can update your class estimates or learn something else on top
of that.
On 02/18/2015 01:31 PM, Pierre-Luc Bacon wrote:
In the field of reinforcement learning (RL), the Fitted-Q algorithm of
Ernst 2005 (http://www.jmlr.org/papers/volume6/ernst05a/ernst05a.pdf)
relies on the ability to fix the tree structure to ensure convergence
(see p. 515 of the JMLR paper).
The warm_start option is useful, but does not fully allow for the
freezing mechanism to take place.
Fitted-Q is highly used in RL and adding a freezing option would
definitely receive a lot of interest. On the other, I understand that
for the sake of keeping the interface general this might not be possible.
My understanding of ``tree.py`` is that such a thing might be
achievable with a custom ``Splitter`` that actually doesn't split
anything but only refreshes the leaves.
Is there an easier workaround ?
Best,
Pierre-Luc
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