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:

F1 = 2 * (precision * recall) / (precision + recall)

In the multi-class and multi-label 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 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 if average 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 in y_true and y_pred are used in sorted order.

Changed in version 0.17: Parameter labels improved for multiclass problem.

pos_labelint, float, bool, str or None, 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; setting labels=[pos_label] and average != 'binary' will report scores for that 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 == 0, precision is undefined. When true positive + false negative == 0, recall is undefined. In such cases, by default the metric will be set to 0, as will f-score, and UndefinedMetricWarning will be raised. This behavior can be modified with zero_division. Note that if zero_division is np.nan, scores being np.nan will be ignored for averaging.

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

Probability Calibration curves

Precision-Recall

Precision-Recall

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset