8.16.1.6. sklearn.metrics.fbeta_score¶
- sklearn.metrics.fbeta_score(y_true, y_pred, beta, pos_label=1)¶
Compute fbeta 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 precision (beta == 0 considers only precision, beta == inf only recall).
Parameters : y_true : array, shape = [n_samples]
true targets
y_pred : array, shape = [n_samples]
predicted targets
beta: float :
pos_label : int
in the binary classification case, give the label of the positive class (default is 1). Everything else but ‘pos_label’ is considered to belong to the negative class. Not used in the case of multiclass classification.
Returns : fbeta_score : float
fbeta_score of the positive class in binary classification or weighted avergage of the fbeta_score of each class for the multiclass task.
See also
R., Addison, pp.
- http
- //en.wikipedia.org/wiki/F1_score