I will try to pick up the work on the one-hot transformer:
https://github.com/scikit-learn/scikit-learn/pull/242
Vlad
On Mar 29, 2012, at 11:36 , Andreas wrote:
> Hi Mohit.
> Generally all algorithms in sklearn assume that all features are continuous.
> Does discrete in your case mean categorial ('0'=blue, '1'=red, '2'=green)?
> Then you should probably
> recode these features using a one-hot encoding as interpreting them as
> continuous will be meaningless.
> I am not sure in what state the functionality for this in sklearn is, maybe
> someone else can comment on that.
>
> If they are discrete but still have an ordering, KMeans might work.
> In these cases I found normalizing them with ``Scaler`` helpful.
>
> Cheers,
> Andy
>
>
> On 03/28/2012 08:14 PM, Mohit Singh wrote:
>> Hi,
>> Does the knn library of scikits deal with features which are both continous
>> and discrete?
>> I have a feature as numpy array.. some of which are real-valued and some are
>> disrete but I havent mentioned anything explicitly??
>> I am not sure whether it will work or not or how will the algorithm know
>> that these are discrete and continous features?
>> Thanks
>>
>> --
>> Mohit
>>
>> "When you want success as badly as you want the air, then you will get it.
>> There is no other secret of success."
>> -Socrates
>>
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