sklearn.metrics
.precision_recall_fscore_support¶

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)¶ Compute precision, recall, Fmeasure 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 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 ifaverage
is one of'micro'
,'macro'
,'weighted'
or'samples'
.Read more in the User Guide.
Parameters: y_true : 1d arraylike, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d arraylike, 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 Fscore.
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 Fscore 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 : arraylike 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
[R268] Wikipedia entry for the Precision and recall [R269] Wikipedia entry for the F1score [R270] Discriminative Methods for Multilabeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 2230 by Shantanu Godbole, Sunita Sarawagi 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') ... # doctest: +ELLIPSIS (0.22..., 0.33..., 0.26..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') ... # doctest: +ELLIPSIS (0.33..., 0.33..., 0.33..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') ... # doctest: +ELLIPSIS (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']) ... # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE (array([0. , 0. , 0.66...]), array([0., 0., 1.]), array([0. , 0. , 0.8]), array([2, 2, 2]))