On 07/20/2016 01:31 PM, Guillaume Lemaître wrote:
Hi Gael,
I was wondering if you could elaborate on the problem of
hyper-parameter tuning and why the imbalanced-learn would not benefit
from it.
Since that we used the identical pipeline of scikit-learn and add the
part to handle the sampler
Hi Gael,
I was wondering if you could elaborate on the problem of hyper-parameter
tuning and why the imbalanced-learn would not benefit from it.
Since that we used the identical pipeline of scikit-learn and add the part
to handle the sampler, I would have think that we could use it.
However this
Hey,
These packages look great! I was interested in the imbalanced learning,
which is something that we stumbled upon:
> * imbalanced-learn: https://github.com/scikit-learn-contrib/imbalanced-learn
> Python module to perform under sampling and over sampling with various
> techniques.
Interestin
Awesome! Thanks to the contributors
On Tue, Jul 19, 2016 at 9:44 PM, Nelson Liu wrote:
> Congrats! These look great, thanks to both the authors and the
> scikit-learn-contrib organizers for putting this together.
>
> Nelson
>
> On Tue, Jul 19, 2016 at 9:09 AM Mathieu Blondel
> wrote:
>
>> Hi ev
Congrats! These look great, thanks to both the authors and the
scikit-learn-contrib organizers for putting this together.
Nelson
On Tue, Jul 19, 2016 at 9:09 AM Mathieu Blondel
wrote:
> Hi everyone,
>
> We are pleased to announce that three new projects recently joined
> scikit-learn-contrib!
>
Hi everyone,
We are pleased to announce that three new projects recently joined
scikit-learn-contrib!
* imbalanced-learn: https://github.com/scikit-learn-contrib/imbalanced-learn
Python module to perform under sampling and over sampling with various
techniques.
* polylearn: https://github.com/s
