[scikit-learn] imbalanced-learn 0.3.0 is chasing scikit-learn 0.19.0

[Brown J.B.]

In drug discovery, if you are lucky you might get hit compounds 10% of the
time.
So if you do ML-based drug discovery, your datasets are strongly imbalanced.
It seems the imbalanced package would be perfect for this area.

J.B.

2017-08-25 10:53 GMT+02:00 Jaques Grobler <jaquesgrobler at gmail.com>:

> Congrats guys!
>
> 2017-08-25 8:18 GMT+02:00 Sebastian Raschka <se.raschka at gmail.com>:
>
>> Just read through the summary of the new features and browsed through the
>> user guide. The guide is really well structured and easy to navigate,
>> thanks for putting all the work into it. Overall, thanks for this great
>> contribution and new version :)
>>
>> Best,
>> Sebastian
>>
>> > On Aug 24, 2017, at 8:14 PM, Guillaume Lemaître <g.lemaitre58 at gmail.com>
>> wrote:
>> >
>> > We are excited to announce the new release of the scikit-learn-contrib
>> imbalanced-learn, already available through conda and pip (cf. the
>> installation page https://tinyurl.com/y92flbab for more info)
>> >
>> > Notable add-ons are:
>> >
>> > * Support of sparse matrices
>> > * Support of multi-class resampling for all methods
>> > * A new BalancedBaggingClassifier using random under-sampling chained
>> with the scikit-learn BaggingClassifier
>> > * Creation of a didactic user guide
>> > * New API of the ratio parameter to fit the needs of multi-class
>> resampling
>> > * Migration from nosetests to pytest
>> >
>> > You can check the full changelog at:
>> > http://contrib.scikit-learn.org/imbalanced-learn/stable/what
>> s_new.html#version-0-3
>> >
>> > A big thank you to contributors to use, raise issues, and submit PRs to
>> imblearn.
>> > --
>> > Guillaume Lemaitre
>> > _______________________________________________
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>> > scikit-learn at python.org
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>>
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