> ## 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.

# Correlated features

> Learn how to use the correlated features test

## Definition

The correlated features test checks if there are features that are strongly correlated with one another.

## Taxonomy

* **Task types**: Tabular classification, tabular regression.
* **Availability**: development (Continuously evaluate your models and datasets as you iterate on their versions.)
  and monitoring (Monitor a model in production, measure its health, check for drifts and set up alerts.).

## Why it matters

* Removing highly correlated features improves model interpretability and can improve generalization performance.
* For some models, [multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) can be an issue, and the coefficients learned are unreliable.
* Sometimes, correlated features can indicate data quality issues -- such as duplicate or near-duplicate columns.

## Test configuration examples

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

```json Development theme={null}
[
  {
    "name": "No highly correlated features",
    "description": "Asserts that there are no highly correlated feature pairs",
    "type": "integrity",
    "subtype": "correlatedFeatureCount",
    "thresholds": [
      {
        "insightName": "correlatedFeatures",
        "measurement": "correlatedFeatureCount",
        "operator": "<=",
        "value": 0
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

```json Monitoring theme={null}
[
  {
    "name": "No highly correlated features",
    "description": "Asserts that there are no highly correlated feature pairs",
    "type": "integrity",
    "subtype": "correlatedFeatureCount",
    "thresholds": [
      {
        "insightName": "correlatedFeatures",
        "measurement": "correlatedFeatureCount",
        "operator": "<=",
        "value": 0
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]
```

## Related

* [Predictive power score (PPS) test](https://docs.openlayer.com/tests/integrity/pp-score-value-validation).


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