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DOC Add examples of make_scorer usage to fbeta_score docstring #28755

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Merged
merged 11 commits into from
Jun 18, 2025
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DOC Add examples of make_scorer usage to fbeta_score docstring #28755

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c025b34
DOC Separate predefined scorer names from the ones requiring make_scorer
marijavlajic Apr 2, 2024
981b7c5
DOC Remove non-implemented scorer names
marijavlajic Apr 2, 2024
bececea
DOC Removed metric functions without user-settable parameters
marijavlajic Apr 2, 2024
08dbee8
DOC Moved the non-implemented scorers table
marijavlajic Apr 2, 2024
45f6e38
Address code review suggestions
marijavlajic Apr 2, 2024
9801391
Fix table formatting
marijavlajic Apr 2, 2024
91e0935
Change power parameter value in examples
marijavlajic Apr 2, 2024
a76ffc4
DOC Add explanation of make_scorer usage (for classification metrics)
marijavlajic Apr 2, 2024
49c1ed3
Add paragraph about make_scorer to the notes and add corresponding ex…
marijavlajic Apr 4, 2024
90161ff
Merge remote-tracking branch 'upstream/main' into doc_makescore_examples
adrinjalali Jun 18, 2025
df0c9fd
docstring
adrinjalali Jun 18, 2025
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29 changes: 27 additions & 2 deletions sklearn/metrics/_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1627,6 +1627,11 @@ def fbeta_score(
returns 0.0 and raises ``UndefinedMetricWarning``. This behavior can be
modified by setting ``zero_division``.

F-beta score is not implemented as a named scorer that can be passed to
the `scoring` parameter of cross-validation tools directly: it requires to be
wrapped with :func:`make_scorer` so as to specify the value of `beta`. See
examples for details.

References
----------
.. [1] R. Baeza-Yates and B. Ribeiro-Neto (2011).
Expand All @@ -1650,9 +1655,29 @@ def fbeta_score(
>>> fbeta_score(y_true, y_pred, average=None, beta=0.5)
array([0.71, 0. , 0. ])
>>> y_pred_empty = [0, 0, 0, 0, 0, 0]
>>> fbeta_score(y_true, y_pred_empty,
... average="macro", zero_division=np.nan, beta=0.5)
>>> fbeta_score(
... y_true,
... y_pred_empty,
... average="macro",
... zero_division=np.nan,
... beta=0.5,
... )
0.128

In order to use :func:`fbeta_scorer` as a scorer, a callable
scorer objects needs to be created first with :func:`make_scorer`,
passing the value for the `beta` parameter.

>>> from sklearn.metrics import fbeta_score, make_scorer
>>> ftwo_scorer = make_scorer(fbeta_score, beta=2)
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.svm import LinearSVC
>>> grid = GridSearchCV(
... LinearSVC(dual="auto"),
... param_grid={'C': [1, 10]},
... scoring=ftwo_scorer,
... cv=5
... )
"""

_, _, f, _ = precision_recall_fscore_support(
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