I'm working with an imbalanced multi-class dataset. I try to tune the parameters of a `DecisionTreeClassifier`, `RandomForestClassifier` and a `GradientBoostingClassifier` using a randomized search and a bayesian search.

For now, I used just `accuracy` for the scoring which is not really applicable for assessing my models performance (which I'm not doing). Is it also not suitable for parameter tuning?

I found that for example `recall_micro` and `recall_weighted` yield the same results as `accuracy`. This should be the same for other metrics like `f1_micro`.

**So my question is**: Is the scoring relevant for tuning? I see that `recall_macro` leads to lower results since it doesn't take the number of samples per class into account. So which metric should I use?