Conversation
beta=0.5 on a precision of 0.75 and a recall of 0.6 returned 1.666667. The denominator multiplied both terms by beta squared.
Thank you for contributing to
|
beta=0.5 on a precision of 0.75 and a recall of 0.6 returned 1.666667. The denominator multiplied both terms by beta squared.
Thank you for contributing to
|
Reference Issues/PRs
None found.
What does this implement/fix? Explain your changes.
range_f_scoredivided bybeta**2 * (precision + recall). The weighted harmonic mean divides bybeta**2 * precision + recall. On the overlapping range already in the tests, precision is0.75and recall is0.6.beta=1is still0.666667.beta=0.5returned1.666667. It is now0.714286.Does your contribution introduce a new dependency? If yes, which one?
No.
Any other comments?
test_range_f_score_beta_is_the_weighted_harmonic_meanfailed on main with1.666667and passes after the change. The other 8 tests intest_range_ts_metrics.pystill pass. The rest of the package pytest suite was not run.PR checklist
For all contributions
This pull request includes code written with the assistance of AI.