sklearn.metrics
.fbeta_score¶
- sklearn.metrics.fbeta_score(y_true, y_pred, *, beta, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')[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 represents the ratio of recall importance to precision importance.beta > 1
gives more weight to recall, whilebeta < 1
favors precision. For example,beta = 2
makes recall twice as important as precision, whilebeta = 0.5
does the opposite. Asymptotically,beta -> +inf
considers only recall, andbeta -> 0
only precision.Read more in the User Guide.
- Parameters:
- y_true1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
- y_pred1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
- betafloat
Determines the weight of recall in the combined score.
- labelsarray-like, default=None
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_labelint, float, bool or str, default=1
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{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’
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
).
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- zero_division{“warn”, 0.0, 1.0, np.nan}, default=”warn”
Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative.
Notes: - If set to “warn”, this acts like 0, but a warning is also raised. - If set to
np.nan
, such values will be excluded from the average.New in version 1.3:
np.nan
option was added.
- Returns:
- fbeta_scorefloat (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.
See also
precision_recall_fscore_support
Compute the precision, recall, F-score, and support.
multilabel_confusion_matrix
Compute a confusion matrix for each class or sample.
Notes
When
true positive + false positive == 0
ortrue positive + false negative == 0
, f-score returns 0 and raisesUndefinedMetricWarning
. This behavior can be modified withzero_division
.References
[1]R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328.
Examples
>>> import numpy as np >>> 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. ]) >>> y_pred_empty = [0, 0, 0, 0, 0, 0] >>> fbeta_score(y_true, y_pred_empty, ... average="macro", zero_division=np.nan, beta=0.5) 0.38...