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sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None)
[source] -
Compute precision, recall, F-measure and support for each class
The precision is the ratio
tp / (tp + fp)
wheretp
is the number of true positives andfp
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)
wheretp
is the number of true positives andfn
the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.
The F-beta 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 F-measure ifaverage
is one of'micro'
,'macro'
,'weighted'
or'samples'
.Read more in the User Guide.
Parameters: y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
beta : float, 1.0 by default
The strength of recall versus precision in the F-score.
labels : list, optional
The set of labels to include when
average != 'binary'
, and their order ifaverage is None
. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels iny_true
andy_pred
are used in sorted order.pos_label : str or int, 1 by default
The class to report if
average='binary'
and the data is binary. If the data are multiclass or multilabel, this will be ignored; settinglabels=[pos_label]
andaverage != 'binary'
will report scores for that label only.average : string, [None (default), ?binary?, ?micro?, ?macro?, ?samples?, ?weighted?]
If
None
, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:-
'binary':
-
Only report results for the class specified by
pos_label
. This is applicable only if targets (y_{true,pred}
) are binary. -
'micro':
-
Calculate metrics globally by counting the total true positives, false negatives and false positives.
-
'macro':
-
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
-
'weighted':
-
Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ?macro? to account for label imbalance; it can result in an F-score that is not between precision and recall.
-
'samples':
-
Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from
accuracy_score
).
warn_for : tuple or set, for internal use
This determines which warnings will be made in the case that this function is being used to return only one of its metrics.
sample_weight : array-like of shape = [n_samples], optional
Sample weights.
Returns: precision : float (if average is not None) or array of float, shape = [n_unique_labels]
recall : float (if average is not None) or array of float, , shape = [n_unique_labels]
fbeta_score : float (if average is not None) or array of float, shape = [n_unique_labels]
support : int (if average is not None) or array of int, shape = [n_unique_labels]
The number of occurrences of each label in
y_true
.References
[R220] Wikipedia entry for the Precision and recall [R221] Wikipedia entry for the F1-score [R222] Discriminative Methods for Multi-labeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu Godbole, Sunita Sarawagi <http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>
Examples
>>> 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 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])) -
sklearn.metrics.precision_recall_fscore_support()
2017-01-15 04:26:40
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