sklearn.metrics.f1_score¶
- sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None)[source]¶
Compute the F1 score, also known as balanced F-score or F-measure
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 multi-class and multi-label case, this is the weighted average of the F1 score of each class.
Read more in the User Guide.
Parameters: y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labels : list, optional
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_label : str or int, 1 by default
The class to report if average='binary'. Until version 0.18 it is necessary to set pos_label=None if seeking to use another averaging method over binary targets.
average : string, [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 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).
Note that if pos_label is given in binary classification with average != ‘binary’, only that positive class is reported. This behavior is deprecated and will change in version 0.18.
sample_weight : array-like of shape = [n_samples], optional
Sample weights.
Returns: f1_score : float 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.
References
[R47] Wikipedia entry for the F1-score 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. ])