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 F-score or F-measure.
The F1 score can be interpreted as a harmonic mean 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:
\[\text{F1} = \frac{2 * \text{TP}}{2 * \text{TP} + \text{FP} + \text{FN}}\]Where \(\text{TP}\) is the number of true positives, \(\text{FN}\) is the number of false negatives, and \(\text{FP}\) is the number of false positives. F1 is by default calculated as 0.0 when there are no true positives, false negatives, or false positives.
Support beyond 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 F1 score forpos_label
. Ifaverage
is not'binary'
,pos_label
is ignored and F1 score for both classes are computed, then averaged or both returned (whenaverage=None
). Similarly, for multiclass and multilabel targets, F1 score for alllabels
are either returned or averaged depending on theaverage
parameter. Uselabels
specify the set of labels to calculate F1 score 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.
- 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.Changed in version 0.17: Parameter
labels
improved for multiclass problem.- 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{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’
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 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
).
- 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, i.e. when all predictions and labels are negative.
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:
- 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.
See also
fbeta_score
Compute the F-beta score.
precision_recall_fscore_support
Compute the precision, recall, F-score, and support.
jaccard_score
Compute the Jaccard similarity coefficient score.
multilabel_confusion_matrix
Compute a confusion matrix for each class or sample.
Notes
When
true positive + false positive + false negative == 0
(i.e. a class is completely absent from bothy_true
ory_pred
), f-score is undefined. In such cases, by default f-score will be set to 0.0, andUndefinedMetricWarning
will be raised. This behavior can be modified by setting thezero_division
parameter.References
Examples
>>> import numpy as np >>> 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. ])
>>> # binary classification >>> y_true_empty = [0, 0, 0, 0, 0, 0] >>> y_pred_empty = [0, 0, 0, 0, 0, 0] >>> f1_score(y_true_empty, y_pred_empty) 0.0... >>> f1_score(y_true_empty, y_pred_empty, zero_division=1.0) 1.0... >>> f1_score(y_true_empty, y_pred_empty, zero_division=np.nan) nan...
>>> # multilabel classification >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]] >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]] >>> f1_score(y_true, y_pred, average=None) array([0.66666667, 1. , 0.66666667])
Examples using sklearn.metrics.f1_score
¶
Probability Calibration curves
Semi-supervised Classification on a Text Dataset