What is a bad, decent, good, and excellent F1-measure range?

Question

I understand F1-measure is a harmonic mean of precision and recall. But what values define how good/bad a F1-measure is? I can't seem to find any references (google or academic) answering my question.

Answer 1

Consider sklearn.dummy.DummyClassifier(strategy='uniform') which is a classifier that make random guesses (a.k.a bad classifier). We can view DummyClassifier as a benchmark to beat, now let's see it's f1-score.

In a binary classification problem, with balanced dataset: 6198 total sample, 3099 samples labelled as 0 and 3099 samples labelled as 1, f1-score is 0.5 for both classes, and weighted average is 0.5:

Second example, using DummyClassifier(strategy='constant'), i.e. guessing the same label every time, guessing label 1 every time in this case, average of f1-scores is 0.33, while f1 for label 0 is 0.00:

I consider these to be bad f1-scores, given the balanced dataset.

PS. summary generated using sklearn.metrics.classification_report