sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=(‘precision’, ’recall’, ’fscore’), sample_weight=None)
[source]
Compute precision, recall, Fmeasure and support for each class
The precision is the ratio tp / (tp + fp)
where tp
is the number of true positives and fp
the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
The recall is the ratio tp / (tp + fn)
where tp
is the number of true positives and fn
the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.
The Fbeta score can be interpreted as a weighted harmonic mean of the precision and recall, where an Fbeta score reaches its best value at 1 and worst score at 0.
The Fbeta score weights recall more than precision by a factor of beta
. beta == 1.0
means recall and precision are equally important.
The support is the number of occurrences of each class in y_true
.
If pos_label is None
and in binary classification, this function returns the average precision, recall and Fmeasure if average
is one of 'micro'
, 'macro'
, 'weighted'
or 'samples'
.
Read more in the User Guide.
Parameters: 


Returns: 

[1]  Wikipedia entry for the Precision and recall 
[2]  Wikipedia entry for the F1score 
[3]  Discriminative Methods for Multilabeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 2230 by Shantanu Godbole, Sunita Sarawagi 
>>> from sklearn.metrics import precision_recall_fscore_support >>> 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') ... (0.22..., 0.33..., 0.26..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') ... (0.33..., 0.33..., 0.33..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') ... (0.22..., 0.33..., 0.26..., None)
It is possible to compute perlabel precisions, recalls, F1scores and supports instead of averaging:
>>> precision_recall_fscore_support(y_true, y_pred, average=None, ... labels=['pig', 'dog', 'cat']) ... (array([0. , 0. , 0.66...]), array([0., 0., 1.]), array([0. , 0. , 0.8]), array([2, 2, 2]))
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Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html