Micro-averaged F1 vs. Accuracy

Given that `f1_score_micro` is basically the same as `accuracy` for multi-class classification problems, is there a reason why we should continue to use that as the default tuning metric rather than just switching to `accuracy`? We can still continue to make `f1_score_micro` available as a metric in SKLL.