Precision Score

---
name: precision-score
description: Compute the precision_score metric — provided by scikit-learn. Use when the user has predictions and ground-truth and needs to compute precision_score, or asks how to score with precision_score.
metadata:
  skill_kind: metric
  source_lib: scikit-learn
  import_path: sklearn.metrics.precision_score
  source: library_introspection
---

# precision-score

> Metric `precision_score` from `scikit-learn` (sklearn.metrics.precision_score)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with precision_score, or
mentions `sklearn.metrics.precision_score` directly, or wants the standard scikit-learn implementation.

## Reference signature

```python
from sklearn.metrics import precision_score

# precision_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')
```

## Library docstring

```
Compute the precision.

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 best value is 1 and the worst value is 0.

Support beyond :term:`binary` targets is achieved by treating :term:`multiclass`
and :term:`multilabel` data as a collection of binary problems, one for each
label. For the :term:`binary` case, setting `average='binary'` will return
precision for `pos_label`. If `average` is not `'binary'`, `pos_label` is ignored
and precision for both classes are computed, then averaged or both returned (when
`average=None`). Similarly, for :term:`multiclass` and :term:`multilabel` targets,
precision for all `labels` are either returned or averaged depending on the
`average` parameter. Use `labels` specify the set of labels to calculate precision
for.

Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.

Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
    Ground truth (correct) target values. Sparse matrix is only supported when
    targets are of :term:`multilabel` type.

y_pred : 1d array-like, or label indicator array / sparse matrix
    Estimated targets as returned by a classifier. Sparse matrix is only
    supported when targets are of :term:`multilabel` type.

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 in multiclass classification to exclude a "negative
    class". Labels not present in the data can be included and will be
    "assigned" 0 samples. For multilabel targets, labels are column indices.
    By default, all labels in `y_true` and `y_pred` are used in sorted order.

    .. versionchanged:: 0.17
       Parameter `labels` improved for multiclass problem.

pos_label : int, float, bool or str, default=1
    The class to report if `average='binary'` and the data is binary,
    otherwise this parameter is ignored.
    For multiclass or multilabel 
```

## Quick recipe

```python
import sklearn.metrics as _m
score = _m.precision_score(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.