Hyperparameter tuning in multiclass classification problem: which scoring metric? 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?
You should use the same metric to evaluate and to tune the classifiers. If you wull evaluate the final classifier using accuracy, then you must use accuracy to tune the hyper parameters. If you think you should use macro-averaged F1 as the final evaluation of the classifier, use it also to tune them.
On a side, for multiclass problems I have not yet heard any convincing argument not to use accuracy, but that is just me.