sklearn.metrics.fbeta_score()

sklearn.metrics.fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None) [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 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).

Read more in the User Guide.

Parameters:

Parameters:	y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, 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 if `average 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 in `y_true` and `y_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; setting `labels=[pos_label]` and `average != '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 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.

y_true : 1d array-like, or label indicator array / sparse matrix

Ground truth (correct) target values.

y_pred : 1d array-like, 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 if average 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 in y_true and y_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; setting labels=[pos_label] and average != '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 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

[R206]

R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328.

[R207]

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.        ])

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html

doc_scikit_learn

2017-01-15 04:26:22

Comments