Andy,

I just recently discussed this with Gilles; There are a number of
things involved here: Gilles told me that his experience shows that
randomized trees are usually deeper than regular trees thus the
increased training time.

After looking at the code I also found that ``_find_random_split``
requires one more pass over the training data compared to
``_find_best_split``. The reason is the following: in order to draw a
random number between [min_x, max_x] one has to determine min_x and
max_x first. This requires one pass over the training data, after the
random split point has been chosen we have to determine the cost of
the split - for this we perform the same linear scan over the input
data as for ``_find_best_split`` but in this case we know exactly
where the split point is.

Apart from that, there are also some variables in
``find_random_split`` which are not ctyped - I've fixed this and
pushed the modifications to master.

best,
 Peter

2012/6/25  <[email protected]>:
> I just read the Post and i was wodering: shouldn't extra trees be faster
> than random forests? In the Blog Post they are slower.
> Andy
> --
> Diese Nachricht wurde von meinem Android-Mobiltelefon mit K-9 Mail gesendet.
>
>
>
> Olivier Grisel <[email protected]> schrieb:
>>
>> Here is the link:
>>
>>
>> http://blog.explainmydata.com/2012/06/ntrain-24853-ntest-25147-ncorrupt.html
>>
>> --
>> Olivier
>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>
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>
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-- 
Peter Prettenhofer

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