I read this paper on a multilabel classification task. The authors evaluate their models on F1-Score but the do not mention if this is the macro, micro or weighted F1-Score. They only mention:
We chose F1 score as the metric for evaluating our multi-label classication system's performance. F1 score is the harmonic mean of precision (the fraction of returned results that are correct) and recall (the frac- tion of correct results that are returned).
From that, can I guess which F1-Score I should use to reproduce their results with scikit-learn? Or is it obvious which one is used by convention?
Edit: I am not sure why this question is marked as off-topic and what would make it on topic, so I try to clarify my question and will be grateful for indications on how and where to ask this qustion.
As I understand it, the difference between the three F1-score calculations is the following:
- macro calculates F1-score for each label and summs them up, with each label the same weight: $f1 = \sum f1_n *\frac{1}{n}$
- weighted calculates F1-score for each label and sums them up multiplied by the support of each label: $f1 = \sum f1_n * w_n$
- micro calculates a total f1-score by calculating precision and recall with the total true positives, false positives and false negatives.
The text in the paper seem to indicate that micro-f1-score is used, because nothing else is mentioned. is it save to think so?