Ah, I understand the concerns much better now. Thank you Lars and Andreas
for taking the time to clear this up for me!
On Mon, Apr 28, 2014 at 10:26 AM, Andreas Mueller <[email protected]> wrote:
> BTW this also needs to go into the non-existing faq.
> On Apr 28, 2014 10:20 AM, "Lars Buitinck" <[email protected]> wrote:
>
>> 2014-04-28 18:56 GMT+02:00 Jacob Schreiber <[email protected]>:
>> > 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.
>>
>> Because we try to provide a unified API for the basic tasks in machine
>> learning, with pipelines and meta-algorithms like grid search to tie
>> everything together. The required concepts, APIs, algorithms and
>> expertise required for stuctured learning are different from what
>> scikit-learn has to offer. If we started doing arbitrary structured
>> learning, we'd need to redesign the whole package and the project
>> would likely collapse under its own weight.
>>
>> That said, there are two project from scikit-learn contributors that
>> do structured prediction:
>>
>> * pystruct [1] by Andreas, Vlad et al. handles general structured
>> learning (focuses on SSVMs on arbitrary graph structures with
>> approximate inference; defines the notion of sample as an instance of
>> the graph structure)
>> * seqlearn [2] by myself and others handles sequences only (focuses on
>> exact inference; has HMMs, but mostly for the sake of completeness;
>> treats a feature vector as a sample and uses an offset encoding for
>> the dependencies between feature vectors)
>>
>> AFAIK, neither has solved the problem of putting a structured output
>> learner at the end of a Pipeline, or putting one inside GridSearchCV.
>>
>> [1] http://pystruct.github.io/
>> [2] http://larsmans.github.io/seqlearn/
>>
>>
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