<p dir="auto">nosetests is no longer maintained and contains deprecated code (inspect.getargspec). It might not keep working with future versions of python. Should we create a plan to move to py.tests?</p>
<h4 dir="auto">Describe the workflow you want to enable</h4>
<p dir="auto">Hi all,<br>
I regularly have to estimate Ridge regressions with a large number of different targets and vastly different signal-to-noise ratios across targets. The optimal regularization per target therefore differs quite a lot. RidgeCV currently estimates the optimal regularization <strong>across all targets</strong>, which can lead to quite sub-optimal performance in the situation I describe.</p>
<h4 dir="auto">Describe your proposed solution</h4>
<p dir="auto">Since RidgeCV evaluates every possible for every target anyway, one can easily get the best alpha per target as well - however, this does not work with GridSearchCV as is. I'm not sure what the best approach to change this would be - I assume it would be necessary to allow multiple scores for a Ridge estimator, as well as adapting ParameterGrid to allow arrays as an individual parameter.<br>
This might warrant too many changes to the inner working of sklearn's model_selection for a use-case that's too specialized (it's hard for me to judge that).<br>
If you think it's worth having this feature, I'm happy to tackle it and discuss what solution might be best.</p>
<h4 dir="auto">Additional context</h4>
<p dir="auto">This problem arises frequently in neuroimaging, where people solve this by either writing their own gridsearch or using individual Ridge estimators per target, but I assume this is a common problem in other fields as well.</p>