Also, taking ownership of hmmlearn and introducing your improvements can
only need to better things!
On Mon, Apr 28, 2014 at 1:36 PM, Jacob Schreiber <[email protected]>wrote:
> 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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