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
.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_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 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_labelint, float, bool or str, default=1
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{‘binary’, ‘micro’, ‘macro’, ‘samples’, ‘weighted’}, default=None
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_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. In such cases, by default the metric will be set to 0, as will f-score, 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]))