AM, Joel Nothman
wrote:
> this is the right place to ask, but I'd be more interested to see a
> scikit-learn-compatible implementation available, perhaps in
> scikit-learn-contrib more than to see it part of the main package...
>
> On 19 Aug 2017 2:13 am, "Michael Capiz
cument your
> estimator(s), and offer it to be housed within scikit-learn-contrib.
>
> On 20 August 2017 at 08:36, Michael Capizzi
> wrote:
>
>> Thanks @joel -
>>
>> I wasn’t aware of scikit-learn-contrib. Is this what you’re referring
>> to? https://github.
I’m wondering if anyone can identify the purpose of this test:
check_classifiers_train(), specifically this line:
https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/utils/estimator_checks.py#L1106
My custom classifier (which I’m hoping to submit to scikit-learn-contrib)
is failing
ite well and data used to train.
> This is true that 83% is empirical but it allows to spot any changes done
> in the algorithms even if the unit tests are passing for some reason.
>
> On 11 October 2017 at 18:52, Michael Capizzi
> wrote:
>
>> I’m wondering if anyone c
above situation qualify?
-M
On Thu, Oct 12, 2017 at 11:27 AM, Michael Capizzi <
mcapi...@email.arizona.edu> wrote:
> Thanks @andreas, for your comments, especially the info that it's the
> `iris` dataset. I have to dig a bit deeper to see what's going on with the
> pe
