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# `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, zero_division='warn')

  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 a negative sample as positive.

  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, default=1.0

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

    labels array-like, default=None

      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 int, 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; setting `labels=[pos_label]` and `average != 'binary'` will report scores for that label only.

    average{‘binary’, ‘micro’, ‘macro’, ‘samples’, ‘weighted’}, default=None

      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 list, 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,), 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:

        - recall: when there are no positive labels

        - precision: when there are no positive predictions

        - f-score: both

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

      Precision score.

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

      Recall score.

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

      F-beta score.

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

      The number of occurrences of each label in `y_true`.

  Notes

  When `true positive + false positive == 0`, precision is undefined. When `true positive + false negative == 0`, recall is undefined. In such cases, by default the metric will be set to 0, as will f-score, and `UndefinedMetricWarning` will be raised. This behavior can be modified with `zero_division`.

  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

      >>> import numpy as np
      >>> 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]))