- 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'.
y_true : array-like or label indicator matrix
Ground truth (correct) target values.
y_pred : array-like or label indicator 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 : array
Integer array of labels.
pos_label : str or int, 1 by default
If average is not None and the classification target is binary, only this class’s scores will be returned.
average : string, [None (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’]
If None, the scores for each class are returned. Otherwise, unless pos_label is given in binary classification, this determines the type of averaging performed on the data:
Calculate metrics globally by counting the total true positives, false negatives and false positives.
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
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.
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
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.
[R165] Wikipedia entry for the Precision and recall [R166] Wikipedia entry for the F1-score [R167] Discriminative Methods for Multi-labeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu Godbole, Sunita Sarawagi <http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>
>>> from sklearn.metrics import precision_recall_fscore_support >>> y_true = np.array([0, 1, 2, 0, 1, 2]) >>> y_pred = np.array([0, 2, 1, 0, 0, 1]) >>> 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)