8000 DOC Clearer and more illustrative description for f_beta_score by pnucci · Pull Request #25548 · scikit-learn/scikit-learn · GitHub
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DOC Clearer and more illustrative description for f_beta_score #25548

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merged 8 commits into from
May 22, 2023
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DOC Clearer and more illustrative description for f_beta_score #25548

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6dd6c67
[MRG] Better documentation for f_beta_score
pnucci Feb 6, 2023
9f08339
Update _classification.py
pnucci Feb 6, 2023
8fd0310
Update sklearn/metrics/_classification.py
pnucci Feb 8, 2023
f007947
Update _classification.py
pnucci Feb 8, 2023
305eb75
Merge branch 'main' into pnucci-fbeta-docs
pnucci Apr 13, 2023
287a972
Merge branch 'main' into pnucci-fbeta-docs
pnucci Apr 17, 2023
3183629
Update _classification.py
pnucci May 6, 2023
5b49de6
Merge branch 'main' into pnucci-fbeta-docs
pnucci May 6, 2023
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10 changes: 6 additions & 4 deletions sklearn/metrics/_classification.py
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Expand Up @@ -1274,10 +1274,12 @@ def fbeta_score(
The F-beta score is the weighted harmonic mean of precision and recall,
reaching its optimal value at 1 and its worst value at 0.

The `beta` pa 6E74 rameter determines the weight of recall in the combined
score. ``beta < 1`` lends more weight to precision, while ``beta > 1``
favors recall (``beta -> 0`` considers only precision, ``beta -> +inf``
only recall).
The `beta` parameter represents the ratio of recall importance to
precision importance. `beta > 1` gives more weight to recall, while
`beta < 1` favors precision. For example, `beta = 2` makes recall twice
as important as precision, while `beta = 0.5` does the opposite.
Asymptotically, `beta -> +inf` considers only recall, and `beta -> 0`
only precision.

Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.

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