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', 'f-score'), sample_weight=None, zero_division='warn')[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 a negative sample as positive.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
.Support beyond term:
binary
targets is achieved by treating multiclass and multilabel data as a collection of binary problems, one for each label. For the binary case, settingaverage='binary'
will return metrics forpos_label
. Ifaverage
is not'binary'
,pos_label
is ignored and metrics for both classes are computed, then averaged or both returned (whenaverage=None
). Similarly, for multiclass and multilabel targets, metrics for alllabels
are either returned or averaged depending on theaverage
parameter. Uselabels
specify the set of labels to calculate metrics for.Read more in the User Guide.
- Parameters:
- y_true1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
- y_pred1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
- betafloat, default=1.0
The strength of recall versus precision in the F-score.
- labelsarray-like, default=None
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 in multiclass classification to exclude a “negative class”. Labels not present in the data can be included and will be “assigned” 0 samples. For multilabel targets, labels are column indices. By default, all labels iny_true
andy_pred
are used in sorted order.- pos_labelint, float, bool or str, default=1
The class to report if
average='binary'
and the data is binary, otherwise this parameter is ignored. For multiclass or multilabel targets, setlabels=[pos_label]
andaverage != 'binary'
to report metrics for one label only.- average{‘binary’, ‘micro’, ‘macro’, ‘samples’, ‘weighted’}, default=None
If
None
, the metrics 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_forlist, 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_weightarray-like of shape (n_samples,), default=None
Sample weights.
- zero_division{“warn”, 0.0, 1.0, np.nan}, default=”warn”
- Sets the value to return when there is a zero division:
recall: when there are no positive labels
precision: when there are no positive predictions
f-score: both
Notes: - If set to “warn”, this acts like 0, but a warning is also raised. - If set to
np.nan
, such values will be excluded from the average.New in version 1.3:
np.nan
option was added.
- Returns:
- precisionfloat (if average is not None) or array of float, shape = [n_unique_labels]
Precision score.
- recallfloat (if average is not None) or array of float, shape = [n_unique_labels]
Recall score.
- fbeta_scorefloat (if average is not None) or array of float, shape = [n_unique_labels]
F-beta score.
- supportNone (if average is not None) or array of int, shape = [n_unique_labels]
The number of occurrences of each label in
y_true
.
Notes
When
true positive + false positive == 0
, precision is undefined. Whentrue positive + false negative == 0
, recall is undefined. Whentrue positive + false negative + false positive == 0
, f-score is undefined. In such cases, by default the metric will be set to 0, andUndefinedMetricWarning
will be raised. This behavior can be modified withzero_division
.References
Examples
>>> import numpy as np >>> 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']) (array([0. , 0. , 0.66...]), array([0., 0., 1.]), array([0. , 0. , 0.8]), array([2, 2, 2]))