Why would you not want to use multiple packages with compatible interfaces?
There are several special purpose libs out there like lightning, seqlearn
and pystruct that extend sklearn. The last two in particular with graphical
models / structured prediction.
The reason not to have everything in one package is that it is hard to
maintain code quality if the maintainers are no users or experts for all
methods. Also the development cycles for sklearn are pretty long now, which
might not be appropriate for new functionality.
Cheers,
Andy
Andy
On Apr 28, 2014 9:57 AM, "Jacob Schreiber" <[email protected]> wrote:
> Thank you for your response, Gaël.
>
> I understand that HMMs do not perform classification in the same manner as
> SVMs or Random Forest, but why is it not desirable to create a new section
> to handle HMMs and possibly other graphical models? They seem like an
> extremely useful and widespread part of machine learning, and I know from
> personal experience that I'd prefer to have all my machine learning from
> the same source if possible.
>
> Jacob
>
>
> On Mon, Apr 28, 2014 at 9:37 AM, Gael Varoquaux <
> [email protected]> wrote:
>
>> On Mon, Apr 28, 2014 at 01:34:14PM -0300, Felipe Eltermann wrote:
>> > Sorry if I missed the discussion, but why is it out of scope?
>>
>> It boils down to the fact that HMM require structure between the samples.
>> The API of scikit-learn has no way of specifying or enforcing this.
>>
>> Gaël
>>
>>
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