Description
Classify one supplied cell under an observed/missing-reason policy.
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.
Classify one supplied cell under an observed/missing-reason policy.
When to use this tool
Use Dataset Cell Missingness Reason Code when you already have observed, reasonCode, allowedMissingCodes and need to classify one supplied cell under an observed/missing-reason policy.
How to use the tool and how it works
- Enter observed, reasonCode, allowedMissingCodes. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read classification. Use the output convention below.
When observed=true require reasonCode exactly empty and return observed; otherwise require exact membership in allowedMissingCodes and return that code.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| observed | Observed | Required. Boolean. |
| reasonCode | Reason Code | Required. Text; maximum UTF-16 length 8192. |
| allowedMissingCodes | Caller-supplied literal vocabulary, which may include an empty code. | Required. Array of text; maximum UTF-16 length 8192; maximum items 256. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| classification | Literal observed for observed cells; otherwise the allowed reason code. A caller-chosen observed missing code can make this label ambiguous. | Required. Text; maximum UTF-16 length 8192. |
{
"input": {
"observed": false,
"reasonCode": "not-collected",
"allowedMissingCodes": [
"not-collected",
"refused"
]
},
"output": {
"classification": "not-collected"
}
}Limitations and assumptions
- The allowed list may itself contain an empty code. No data value, missingness mechanism, complete vocabulary or suitable imputation is inferred. A missing code equal to observed can collide with the observed-state label.
- 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
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