I wonder what their core tree algorithms are and how fast they are. It
seems they're achieving those speeds by either
A) Significantly optimizing the tree learner
or
B) Using a smart cutoff on the size of the forest based

Tommy Guy

On Mon, Aug 27, 2012 at 10:14 AM, Jaques Grobler
<[email protected]> wrote:
> cool little interactive benchmark. Wonder what they do RF wise to train so
> damn quickly..
> clever optimaztion or something?
> Agree regarding the other datasets
>
>
> 2012/8/27 Gilles Louppe <[email protected]>
>>
>> (However, I'd be curious to see the same benchmark on other datasets,
>> with a larger number of features...)
>>
>> On 27 August 2012 09:13, Gilles Louppe <[email protected]> wrote:
>> > Hi Peter,
>> >
>> > At least we are better than Weka! More seriously, this indeed shows
>> > that there is still a lot of work to do... :(
>> >
>> > Gilles
>> >
>> > On 27 August 2012 09:06, Peter Prettenhofer
>> > <[email protected]> wrote:
>> >> Hi folks,
>> >>
>> >> I just stumbled upon this benchmark comparing wiserf, R randomForest,
>> >> sklearn (0.12), and weka:
>> >>
>> >> http://wise.io/wiserf.html
>> >>
>> >> Lets put it that way: I think we can/should do better ;-)
>> >>
>> >> best,
>> >>  Peter
>> >>
>> >> --
>> >> Peter Prettenhofer
>> >>
>> >>
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