[scikit-learn] Cross-validation & cross-testing

[Joel Nothman]

And when I mean testing it, I mean writing tests that live with the code so
that they can be re-executed, and so that someone else can see what your
tests assert about your code's correctness.

On 5 June 2017 at 11:52, Joel Nothman <joel.nothman at gmail.com> wrote:

> Hi Rain,
>
> I would suggest that you start by documenting what your code is meant to
> do (the structure of the Korjus et al paper makes it pretty difficult to
> even determine what this technique is, for you to then not to describe it
> in your own words in your repository), testing it with diverse inputs and
> ensuring that it is correct. At a glance I can see at least two sources of
> bugs, and some API design choices which I think could be improved.
>
> Cheers,
>
> Joel
>
> On 5 June 2017 at 07:04, Rain Vagel <rain.vagel at gmail.com> wrote:
>
>> Hey,
>>
>> I am a bachelor’s student and for my thesis I implemented a cross-testing
>> function in a scikit-learn compatible way and published it on Github. The
>> paper on which I based my own thesis can be found here:
>> http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0161788.
>>
>> My project can be found here: https://github.com/RainVagel/c
>> ross-val-cross-test.
>>
>> Our original plan was to try and get the algorithm into scikit-learn, but
>> it doesn’t meet the requirements yet. So instead we thought about maybe
>> having it listed in the “Related Projects” page. Is it possible for
>> somebody to take a look and give any feedback?
>>
>> Sincerely,
>> Rain
>>
>>
>>
>>
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>>
>>
>
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