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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