sklearn.metrics.precision_recall_fscore_support

sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None)

Compute precision, recall, F-measure and support for each class

The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.

The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.

The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.

The F-beta score weights recall more than precision by a factor of beta. beta == 1.0 means recall and precision are equally important.

The support is the number of occurrences of each class in y_true.

If pos_label is None and in binary classification, this function returns the average precision, recall and F-measure if average is one of 'micro', 'macro', 'weighted' or 'samples'.

Read more in the User Guide.

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, 1.0 by default

The strength of recall versus precision in the F-score.

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.

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 (default), ‘binary’, ‘micro’, ‘macro’, ‘samples’, ‘weighted’]

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).

warn_for : tuple or set, for internal use

This determines which warnings will be made in the case that this function is being used to return only one of its metrics.

sample_weight : array-like of shape = [n_samples], optional

Sample weights.

Returns:

precision : float (if average is not None) or array of float, shape = [n_unique_labels]

recall : float (if average is not None) or array of float, , shape = [n_unique_labels]

fbeta_score : float (if average is not None) or array of float, shape = [n_unique_labels]

support : int (if average is not None) or array of int, shape = [n_unique_labels]

The number of occurrences of each label in y_true.

References

[1]	Wikipedia entry for the Precision and recall

[2]	Wikipedia entry for the F1-score

[3]	Discriminative Methods for Multi-labeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu Godbole, Sunita Sarawagi

Examples

>>> from sklearn.metrics import precision_recall_fscore_support
>>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
>>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
>>> precision_recall_fscore_support(y_true, y_pred, average='macro')
... 
(0.22..., 0.33..., 0.26..., None)
>>> precision_recall_fscore_support(y_true, y_pred, average='micro')
... 
(0.33..., 0.33..., 0.33..., None)
>>> precision_recall_fscore_support(y_true, y_pred, average='weighted')
... 
(0.22..., 0.33..., 0.26..., None)

It is possible to compute per-label precisions, recalls, F1-scores and supports instead of averaging:

>>> precision_recall_fscore_support(y_true, y_pred, average=None,
... labels=['pig', 'dog', 'cat'])
... 
(array([0.        , 0.        , 0.66...]),
 array([0., 0., 1.]), array([0. , 0. , 0.8]),
 array([2, 2, 2]))