Micro Average vs Macro Average for Class Imbalance
The question is actually about understanding what it means to &quot;take imbalance into account&quot;:

Micro-average &quot;takes imbalance into account&quot; in the sense that the resulting performance is based on the proportion of every class, i.e. the performance of a large class has more impact on the result than of a small class.
Macro-average &quot;doesn't take imbalance into account&quot; in the sense that the resulting performance is a simple average over the classes, so every class is given equal weight independently from their proportion.

Is it actually a good idea to &quot;take imbalance into account&quot;? It depends:

With micro-average, a classifier is encouraged to focus on the largest classes, possibly at the expense of the smallest ones. This can be considered a positive because it means that more instances will be predicted correctly.
With macro-average, a classifier is encouraged to try to recognize every class correctly. Since it is usually harder for the classifier to identify the small classes, this often makes it sacrifice some performance on the large classes. This can be considered a positive in the sense that it forces the classifier to properly distinguish the classes instead of lazily relying on the distribution of classes.

One could say that it's a kind of quantity vs. quality dilemma: micro-average gives more correct predictions, macro-average gives attention to actually distinguishing the classes.
Very often one uses macro with strongly imbalanced data, because otherwise (with micro) it's too easy for the classifier to obtain a good performance by relying only on the majority class. Your data is not strongly imbalanced so it's unlikely this would happen, but I think I would still opt for macro here.