8.17.1.6. sklearn.metrics.fbeta_score¶
- sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='weighted')¶
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 :
Weight of precision in harmonic mean.
labels : array
Integer array of labels
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. Set to None in the case of multiclass classification.
average : string, [None, ‘micro’, ‘macro’, ‘weighted’(default)]
In the multiclass classification case, this determines the type of averaging performed on the data.
- macro:
Average over classes (does not take imbalance into account).
- micro:
Average over instances (takes imbalance into account). This implies that precision == recall == f1
- weighted:
Average weighted by support (takes imbalance into account). Can result in f1 score that is not between precision and recall.
Returns : fbeta_score : float
fbeta_score of the positive class in binary classification or weighted average of the fbeta_score of each class for the multiclass task.
References
R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328.