The feature values test allows you to define the expected range of values for a feature. For categorical features,
you can define the expected categories.
Ensuring that the values of a feature are always within a defined range is important to validate the hypotheses around the data. For example, for a feature such as Age, negative values would be invalid and signal an issue with the data collection/ingestion process.
Values outside the expected range can also be a sign of data drift.
For some categorical features, it is important to ensure that the values are always within the expected categories.
If you are writing a tests.json, here are a few valid configurations for the character length test:
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[ { "name": "Feature 'Year' less than 2026", "description": "Asserts that the values of the feature 'Year' are within the specified range", "type": "integrity", "subtype": "featureValueValidation", "thresholds": [ { "insightName": "featureProfile", "insightParameters": [ { "name": "name", "value": "Year" }, // Selects feature `Year` ], "measurement": "max", // Must be one of `max` or `min` "operator": "<=", "value": 2026 } ], "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 }]