Description
Check whether one supplied identifier appears in both training and test lists.
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.
Check whether one supplied identifier appears in both training and test lists.
When to use this tool
Use Train Test Single Identifier Collision when you already have identifier, trainingIds, testIds and need to check whether one supplied identifier appears in both training and test lists.
How to use the tool and how it works
- Enter identifier, trainingIds, testIds. Follow any array-alignment requirements below. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read inTraining, inTest, collision. Inspect inTraining, inTest, collision before interpreting the associated values.
Use literal includes on each list; return both membership flags and their conjunction as collision.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| identifier | Identifier | Required. Text; maximum UTF-16 length 8192. |
| trainingIds | Training Ids | Required. Array of text; maximum UTF-16 length 8192; maximum items 256. |
| testIds | Test Ids | Required. Array of text; maximum UTF-16 length 8192; maximum items 256. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| inTraining | In Training | Required. Boolean. |
| inTest | In Test | Required. Boolean. |
| collision | One identifier present in both supplied lists; not a full train/test leakage audit. | Required. Boolean. |
{
"input": {
"identifier": "R17",
"trainingIds": [
"R17",
"R2"
],
"testIds": [
"R17"
]
},
"output": {
"inTraining": true,
"inTest": true,
"collision": true
}
}Limitations and assumptions
- Empty IDs and duplicate entries are accepted. This checks one string only, not full-split leakage, related entities, target leakage or model validity.
- 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
- Feature Value Unseen Category Flag
Flag a category absent from a supplied training-category list.
- Target Label Availability Date Flag
Check a label-availability date against a training cutoff under an equality policy.