> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Precision

> Learn how to use the precision test

## Definition

The precision test measures the accuracy of positive predictions, calculated as TP / (TP + FP). For binary classification, it considers class 1 as "positive." For multiclass classification, it uses the macro-average of the precision score for each class, treating all classes equally.

## Taxonomy

* **Task types**: Tabular classification, text classification.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>
  and <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip>.

## Why it matters

* Precision measures how many of the predicted positive cases are actually positive, making it crucial when false positives are costly.
* It's particularly important in applications like spam detection, medical diagnosis, or fraud detection where incorrect positive predictions can have serious consequences.
* Higher precision values indicate better model performance, with 1.0 representing no false positives.
* Precision complements recall to provide a complete picture of model performance on positive class predictions.

## Required columns

To compute this metric, your dataset must contain the following columns:

* **Predictions**: The predicted class labels from your classification model
* **Ground truths**: The actual/true class labels

## Test configuration examples

If you are writing a `tests.json`, here are a few valid configurations for the precision test:

<CodeGroup>
  ```json Development theme={null}
  [
    {
      "name": "Precision above 0.8",
      "description": "Ensure that the precision is above 0.8",
      "type": "performance",
      "subtype": "metricThreshold",
      "thresholds": [
        {
          "insightName": "metrics",
          "insightParameters": null,
          "measurement": "precision",
          "operator": ">",
          "value": 0.8
        }
      ],
      "subpopulationFilters": null,
      "mode": "development",
      "usesValidationDataset": true,
      "usesTrainingDataset": false,
      "usesMlModel": true,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
    }
  ]
  ```

  ```json Monitoring theme={null}
  [
    {
      "name": "Precision above 0.8",
      "description": "Ensure that the precision is above 0.8",
      "type": "performance",
      "subtype": "metricThreshold",
      "thresholds": [
        {
          "insightName": "metrics",
          "insightParameters": null,
          "measurement": "precision",
          "operator": ">",
          "value": 0.8
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 3600,
      "delayWindow": 0,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
    }
  ]
  ```
</CodeGroup>

## Related

* [Recall test](/tests/catalog/recall) - Measure ability to find all positive instances.
* [F1 test](/tests/catalog/f1) - Harmonic mean of precision and recall.
* [False positive rate test](/tests/catalog/false-positive-rate) - Measure incorrect positive predictions.
* [Accuracy test](/tests/catalog/accuracy) - Overall classification correctness.
* [Aggregate metrics](/tests/performance/aggregate-metrics) - Overview of all available metrics.