Description
Label a feature's supplied reference level with an unambiguous quoted value.
Data workflows require explicit training scope, feature/label availability, missingness codes, group assignments and evaluation direction. Supplied training statistics are not proof that preprocessing avoided leakage. A local target-encoding term or retransformed prediction depends on assumptions that must be checked in the complete model and validation design.
Label a feature's supplied reference level with an unambiguous quoted value.
When to use this tool
Use Categorical Level Reference Label when you already have featureName, referenceLevel and need to label a feature's supplied reference level with an unambiguous quoted value.
How to use the tool and how it works
- Enter featureName, referenceLevel. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read label. Use the output convention below.
Require featureName nonempty; quote referenceLevel with JSON.stringify inside the reference label.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| featureName | Feature Name | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| referenceLevel | Reference Level | Required. Text; maximum UTF-16 length 8192. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| label | Label | Required. Text; maximum UTF-16 length 8192. |
{
"input": {
"featureName": "treatment",
"referenceLevel": "control"
},
"output": {
"label": "treatment [reference=\"control\"]"
}
}Limitations and assumptions
- The reference level may be empty. No category is verified, contrast selected, encoding performed or model fit checked.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Alternative or Complementary methods
Review the full data lineage, preprocessing fit, split design and evaluation procedure for model conclusions. Estimate required statistics only within the intended training folds and verify actual availability; local supplied-value transformations cannot establish those properties by themselves.
References
Similar or alternative tools
- One Hot Reference Level Drop Flag
Flag literal equality with a supplied one-hot reference category.
- Feature Value Unseen Category Flag
Flag a category absent from a supplied training-category list.