On 02/04/2013 04:02 PM, Ribonous wrote:
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?
The random forest part is only Python, the trees are partially (mainly)
in Cython.
In the multi-output case, I am not 100% sure but quite sure that the
criterion is simply summed over the outputs,
i.e. something like the deviance of the hamming-loss.
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