[scikit-learn] ANN Scikit-learn 0.18 released

[Andreas Mueller]

On 09/29/2016 11:07 PM, Joel Nothman wrote:
> (this has been in drafts a few days and I'm sure there's plenty I've 
> missed from the lists below)
>
> Well done, everyone! The size of this release - and the group of 
> people that contributed to it - is even a bit overwhelming. Thanks for 
> managing the release, Andy... and writing it up as a book!
Thank you for your incredible dedication!

>
> We've got a lot in the works already for 0.19.
>
> There are a number of things that have been a long time coming and 
> which I'd really like to see happen, such as:
>
> * multiple metrics for cross validation (#7388 et al.)
> * documenting and officially making (most) utils public (#6616)
> * indicator features for Imputer, done right (#6556)
> * KNN imputation (#2989, #4844)
> * ColumnTransformer or similar for heterogeneous data (#2034, #886)
> * dataset resampling (#1454)
> * string handling in OneHotEncoder (#7327)
> * interpolation in average_precision_score (#7356)
> * tree categorical splits (#4899)
> * k-best feature selection from a model's feature_importances_ (#6717)
> * ? feature name transformation (#6425)
> * ? sample_weight support in CV scoring (#1179, #2879, #3524, #1574; 
> perhaps this isn't as easy as it looks)
>
> There are things that are important but will probably require more work:
>
> * making common tests and their exceptions more general (perhaps by 
> way of "estimator tags")
> * improving our LSH offerings and integration
>
It's good to see that you're excited about the same things as me.

I also want to do the numpy-doc update, as it gives SOOO much better 
error messages now.

I'll try to put some time into the public utils soon, and I think the 
interpolation in average precision is basically done!
I think many of the other things you mentioned are already well on their 
way, and maybe we can get 0.19 out within the next 4 month,
to get back on a more regular schedule.