can anyone give me a sample algorithm for one hot encoding used in
scikit-learn?
On Thu, Jun 20, 2013 at 8:37 PM, Peter Prettenhofer <
peter.prettenho...@gmail.com> wrote:
> you can try an ordinal encoding instead - just map each categorical value
> to an integer so that you end up with 8 numerical features - if you use
> enough trees and grow them deep it may work
>
>
> 2013/6/20 Maheshakya Wijewardena <pmaheshak...@gmail.com>
>
>> And yes Gilles, It is the Amazon challenge :D
>>
>>
>> On Thu, Jun 20, 2013 at 8:21 PM, Maheshakya Wijewardena <
>> pmaheshak...@gmail.com> wrote:
>>
>>> The shape of X after encoding is (32769, 16600). Seems as if that is too
>>> big to be converted into a dense matrix. Can Random forest handle this
>>> amount of features?
>>>
>>>
>>> On Thu, Jun 20, 2013 at 7:31 PM, Olivier Grisel <
>>> olivier.gri...@ensta.org> wrote:
>>>
>>>> 2013/6/20 Lars Buitinck <l.j.buiti...@uva.nl>:
>>>> > 2013/6/20 Olivier Grisel <olivier.gri...@ensta.org>:
>>>> >>> Actually twice as much, even on a 32-bit platform (float size is
>>>> >>> always 64 bits).
>>>> >>
>>>> >> The decision tree code always uses 32 bits floats:
>>>> >>
>>>> >>
>>>> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_tree.pyx#L38
>>>> >>
>>>> >> but you have to cast your data to `dtype=np.float32` in fortran
>>>> layout
>>>> >> ahead of time to avoid the memory copy.
>>>> >
>>>> > OneHot produces np.float, though, which is float64.
>>>>
>>>> Alright but you could convert it to np.float32 before calling toarray.
>>>> But anyway this kind of sparsity level is unsuitable for random
>>>> forests anyways I think.
>>>>
>>>> --
>>>> Olivier
>>>> http://twitter.com/ogrisel - http://github.com/ogrisel
>>>>
>>>>
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>>>
>>>
>>
>>
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>
>
> --
> Peter Prettenhofer
>
>
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