FIX use balanced accuracy from scikit-learn

[glemaitre]

closes #127

[kegl]

Can you check on the mouse cytometry starting kit if the new score is equivalent to the old score? Also maybe sampling some of the user submissions. https://www.ramp.studio/problems/mouse_cytometry

[glemaitre]

In scikit-learn, they consider this score meaningful only in binary classification. So it will not work for multiclass.

[glemaitre]

In fact, this the reason it is failing on iris (more over iris is fully balanced so not sure it is meaninful to use it there)

[glemaitre]

And the discussion to make it multiclass is there:
scikit-learn/scikit-learn#10587

[kegl]

I don't want to get into these debates now, just make sure it is backward compatible. It would probably be better to create a new score, like balanced_accuracy_sk or something like that.

[glemaitre]

I checked. It is back compatible for multi-class apparently and does not give bad results for the binary case. But I would need a bit more time to check.

[glemaitre]

Basically the only thing is to expose the adjusted parameter. True will fall back to the previous implementation while False will not adjust for chance.

[kegl]

ok

[codecov]

Codecov Report

Merging #128 into master will decrease coverage by 0.03%.
The diff coverage is 90.47%.

@@            Coverage Diff             @@
##           master     #128      +/-   ##
==========================================
- Coverage   93.82%   93.78%   -0.04%     
==========================================
  Files         101      101              
  Lines        3092     3107      +15     
==========================================
+ Hits         2901     2914      +13     
- Misses        191      193       +2

Impacted Files	Coverage Δ
rampwf/score_types/balanced_accuracy.py	93.33% <90.47%> (-6.67%)	⬇️

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