? 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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