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) 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 a negative sample as positive.

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 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, setting average='binary' will return metrics for pos_label. If average is not 'binary', pos_label is ignored and metrics for both classes are computed, then averaged or both returned (when average=None). Similarly, for multiclass and multilabel targets, metrics for all labels are either returned or averaged depending on the average parameter. Use labels specify the set of labels to calculate metrics for.

Read more in the User Guide.

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 if average 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 in y_true and y_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, set labels=[pos_label] and average != '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:


Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.


Calculate metrics globally by counting the total true positives, false negatives and false positives.


Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.


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.


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.

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.


When true positive + false positive == 0, precision is undefined. When true positive + false negative == 0, recall is undefined. When true positive + false negative + false positive == 0, f-score is undefined. In such cases, by default the metric will be set to 0, and UndefinedMetricWarning will be raised. This behavior can be modified with zero_division.



>>> 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]))