sklearn.metrics.fbeta_score¶
- sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='weighted', sample_weight=None)¶
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).
Parameters: y_true : array-like or label indicator matrix
Ground truth (correct) target values.
y_pred : array-like or label indicator matrix
Estimated targets as returned by a classifier.
beta: float :
Weight of precision in harmonic mean.
labels : array
Integer array of labels.
pos_label : str or int, 1 by default
If average is not None and the classification target is binary, only this class’s scores will be returned.
average : string, [None, ‘micro’, ‘macro’, ‘samples’, ‘weighted’ (default)]
If None, the scores for each class are returned. Otherwise, unless pos_label is given in binary classification, this determines the type of averaging performed on the data:
- '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_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
[R150] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328. [R151] 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. ])