8000 [MRG + 1] Add example for precision_recall_fscore_support by Flimm · Pull Request #4253 · scikit-learn/scikit-learn · GitHub
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[MRG + 1] Add example for precision_recall_fscore_support #4253

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Merged
merged 3 commits into from
Aug 30, 2015
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[MRG + 1] Add example for precision_recall_fscore_support #4253

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2e414d7
Add example for precision_recall_fscore_support
Flimm Feb 16, 2015
b649df6
Shrink the doc for precision_recall_fscore_support
Flimm Feb 18, 2015
1250974
Shrink precision_recall_fscore_support's doc again
Flimm Feb 19, 2015
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14 changes: 12 additions & 2 deletions sklearn/metrics/classification.py
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Expand Up @@ -811,8 +811,8 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
Examples
--------
>>> from sklearn.metrics import precision_recall_fscore_support
>>> y_true = np.array([0, 1, 2, 0, 1, 2])
>>> y_pred = np.array([0, 2, 1, 0, 0, 1])
>>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
>>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
>>> precision_recall_fscore_support(y_true, y_pred, average='macro')
... # doctest: +ELLIPSIS
(0.22..., 0.33..., 0.26..., None)
Expand All @@ -823,6 +823,16 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
... # doctest: +ELLIPSIS
(0.22..., 0.33..., 0.26..., None)

It is possible to compute per-label precisions, recalls, F1-scores and
supports instead of averaging:
>>> precision_recall_fscore_support(y_true, y_pred, average=None,
... labels=['pig', 'dog', 'cat'])
... # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE
(array([ 0. , 0. , 0.66...]),
array([ 0., 0., 1.]),
array([ 0. , 0. , 0.8]),
array([2, 2, 2]))

"""
average_options = (None, 'micro', 'macro', 'weighted', 'samples')
if average not in average_options and average != 'binary':
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