I think I understand how a random forest classifier works in the univariate
case. Unfortunately I haven't found much information about how to implement
random forest classifier in the multi-output case.
How does the random forest classifier in sklearn measure the information
gain for a given split in the *multi-output case* ? Can anyone point me to
references on this?
Also, is the random forest implementation written in Python or another
language?
Thanks,
Lukas
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