Correlated features

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Definition

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

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Taxonomy

• Category: Integrity.
• Task types: Tabular classification, tabular regression.
• Availability: development and monitoring.

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Why it matters

• Removing highly correlated features improves model interpretability and can improve generalization performance.
• For some models, 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.

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Test configuration examples

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

[
  {
    "name": "No highly correlated features",
    "description": "Asserts that there are no highly correlated feature pairs",
    "type": "integrity",
    "subtype": "correlatedFeatureCount",
    "thresholds": [
      {
        "insightName": "correlatedFeatures",
        "insightParameters": null,
        "measurement": "correlatedFeatureCount",
        "operator": "<=",
        "value": 0
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true, // Apply test to the validation set
    "usesTrainingDataset": false,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689" // Some unique id
  }
]

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Related

• Predictive power score (PPS) test.