I'd like to analyse a bit and encode using that method to cohere with
random forests in scikit-learn.
On Fri, Jun 21, 2013 at 2:08 PM, Peter Prettenhofer <
peter.prettenho...@gmail.com> wrote:
> ? you already use one-hot encoding in your example (
> preprocessing.OneHotEncoder)
>
>
> 2013/6/21 Maheshakya Wijewardena <pmaheshak...@gmail.com>
>
>> 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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>
>
> --
> Peter Prettenhofer
>
>
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