sklearn.metrics.fbeta_score¶
- sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)[source]¶
Compute the F-beta score
The F-beta 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 precision in the combined score. beta < 1 lends more weight to precision, while beta > 1 favors recall (beta -> 0 considers only precision, beta -> inf only recall).
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.
beta: float :
Weight of precision in harmonic mean.
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: fbeta_score : float (if average is not None) or array of float, shape = [n_unique_labels]
F-beta score of the positive class in binary classification or weighted average of the F-beta score of each class for the multiclass task.
References
[R163] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328. [R164] Wikipedia entry for the F1-score 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. ])