sklearn.metrics
.f1_score¶

sklearn.metrics.
f1_score
(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')[source]¶ Compute the F1 score, also known as balanced Fscore or Fmeasure
The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:
F1 = 2 * (precision * recall) / (precision + recall)
In the multiclass and multilabel case, this is the average of the F1 score of each class with weighting depending on the
average
parameter.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.
 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
 f1_scorefloat or array of float, shape = [n_unique_labels]
F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task.
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 fscore, andUndefinedMetricWarning
will be raised. This behavior can be modified withzero_division
.References
Examples
>>> from sklearn.metrics import f1_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> f1_score(y_true, y_pred, average='macro') 0.26... >>> f1_score(y_true, y_pred, average='micro') 0.33... >>> f1_score(y_true, y_pred, average='weighted') 0.26... >>> f1_score(y_true, y_pred, average=None) array([0.8, 0. , 0. ]) >>> y_true = [0, 0, 0, 0, 0, 0] >>> y_pred = [0, 0, 0, 0, 0, 0] >>> f1_score(y_true, y_pred, zero_division=1) 1.0...