# tf.compat.v1.metrics.precision

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/metrics_impl.py#L2136-L2228) |

Computes the precision of the predictions with respect to the labels.

    tf.compat.v1.metrics.precision(
        labels,
        predictions,
        weights=None,
        metrics_collections=None,
        updates_collections=None,
        name=None
    )

The `precision` function creates two local variables,
`true_positives` and `false_positives`, that are used to compute the
precision. This value is ultimately returned as `precision`, an idempotent
operation that simply divides `true_positives` by the sum of `true_positives`
and `false_positives`.

For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`precision`. `update_op` weights each prediction by the corresponding value in
`weights`.

If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

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| ## Args ||
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| `labels` | The ground truth values, a `Tensor` whose dimensions must match `predictions`. Will be cast to `bool`. |
| `predictions` | The predicted values, a `Tensor` of arbitrary dimensions. Will be cast to `bool`. |
| `weights` | Optional `Tensor` whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `labels` dimension). |
| `metrics_collections` | An optional list of collections that `precision` should be added to. |
| `updates_collections` | An optional list of collections that `update_op` should be added to. |
| `name` | An optional variable_scope name. |

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| ## Returns ||
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| `precision` | Scalar float `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_positives`. |
| `update_op` | `Operation` that increments `true_positives` and `false_positives` variables appropriately and whose value matches `precision`. |

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| ## Raises ||
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| `ValueError` | If `predictions` and `labels` have mismatched shapes, or if `weights` is not `None` and its shape doesn't match `predictions`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. |
| `RuntimeError` | If eager execution is enabled. |

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