On Wed, Dec 3, 2014 at 4:09 PM, Joel Nothman <joel.noth...@gmail.com> wrote:
> Hi Tom,
>
> Anyone is welcome to publish their implementations in a format compatible
> with scikit-learn's estimators. However, the centralised project already
> takes a vast amount of work (almost all of it unpaid) to maintain, even
> while adopting a very restrictive scope. Incorporating less-established
> techniques provides marginal benefit for huge costs, exacerbating the
> potential for code rot and maintainer exhaustion.
>
> That being said, the rule of thumb is only a rule of thumb. Counting
> citations for a technique is not always straightforward, and it's often
> practical to implement a recent variant of a well-established technique.
> For example, LSH Forest is being adopted as a practical (in terms of free
> parameters) variant of Locality Sensitive Hashing, although the LSH Forest
> technique has only received <200 citations in 9 years. Even this is done at
> some risk of the technique being superseded in the immediate term.
>
I think 1000 citations is a bit too much to ask. We should probably update
the FAQ with something more reasonable, like say 200 citations. That said,
I agree that the citation threshold is just an indicator. For example, SAG
and AdaGrad, which are considerely considered for inclusion, have around 75
and 250 citations currently.
> I'm not certain what qualifies as rule learning. But the 2000+ citations
> of Liu et al's (1998) "Integrating classification and association rule
> mining" suggest that this technique or perhaps a recent variant would be
> welcome.
>
> Perhaps scikit-learn needs to strengthen and formalise its support for
> external related projects that adopt its API design to implement less
> established techniques. The listing at
> https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets
> lacks glamour, and could be easier to find and navigate.
>
+1
We need to bring this page to the main documentation and make it more sexy.
M.
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