sklearn.metrics
.fbeta_score¶

sklearn.metrics.
fbeta_score
(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None)¶ Compute the Fbeta score
The Fbeta 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, whilebeta > 1
favors recall (beta > 0
considers only precision,beta > inf
only recall).Read more in the User Guide.
Parameters: y_true : 1d arraylike, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d arraylike, 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 ifaverage 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 iny_true
andy_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'
and the data is binary. If the data are multiclass or multilabel, this will be ignored; settinglabels=[pos_label]
andaverage != 'binary'
will report scores for that label only.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 Fscore 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 : arraylike of shape = [n_samples], optional
Sample weights.
Returns: fbeta_score : float (if average is not None) or array of float, shape = [n_unique_labels]
Fbeta score of the positive class in binary classification or weighted average of the Fbeta score of each class for the multiclass task.
References
[R252] R. BaezaYates and B. RibeiroNeto (2011). Modern Information Retrieval. Addison Wesley, pp. 327328. [R253] Wikipedia entry for the F1score 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) ... # doctest: +ELLIPSIS 0.23... >>> fbeta_score(y_true, y_pred, average='micro', beta=0.5) ... # doctest: +ELLIPSIS 0.33... >>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5) ... # doctest: +ELLIPSIS 0.23... >>> fbeta_score(y_true, y_pred, average=None, beta=0.5) ... # doctest: +ELLIPSIS array([0.71..., 0. , 0. ])