sklearn.metrics
.fbeta_score¶

sklearn.metrics.
fbeta_score
(y_true, y_pred, *, beta, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')[source]¶ Compute the Fbeta score
The Fbeta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0.
The
beta
parameter determines the weight of recall in the combined score.beta < 1
lends more weight to precision, whilebeta > 1
favors recall (beta > 0
considers only precision,beta > +inf
only recall).Read more in the User Guide.
 Parameters
 y_true1d arraylike, or label indicator array / sparse matrix
Ground truth (correct) target values.
 y_pred1d arraylike, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
 betafloat
Determines the weight of recall in the combined score.
 labelslist, 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.Changed in version 0.17: parameter labels improved for multiclass problem.
 pos_labelstr 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. averagestring, [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’]
This parameter is required for multiclass/multilabel targets. 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
).
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 zero_division“warn”, 0 or 1, default=”warn”
Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative. If set to “warn”, this acts as 0, but warnings are also raised.
 Returns
 fbeta_scorefloat (if average is not None) or array of float, shape = [n_unique_labels]
Fbeta score of the positive class in binary classification or weighted average of the Fbeta score of each class for the multiclass task.
Notes
When
true positive + false positive == 0
ortrue positive + false negative == 0
, fscore returns 0 and raisesUndefinedMetricWarning
. This behavior can be modified withzero_division
.References
 1
R. BaezaYates and B. RibeiroNeto (2011). Modern Information Retrieval. Addison Wesley, pp. 327328.
 2
Examples
>>> from sklearn.metrics import fbeta_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> fbeta_score(y_true, y_pred, average='macro', beta=0.5) 0.23... >>> fbeta_score(y_true, y_pred, average='micro', beta=0.5) 0.33... >>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5) 0.23... >>> fbeta_score(y_true, y_pred, average=None, beta=0.5) array([0.71..., 0. , 0. ])