Description
Scale a value against training bounds and report range excess.
Feature transformations depend on training-derived parameters, category vocabularies and explicit missing-value conventions. A local formula or token rule does not ensure that fitting, application and validation avoid data leakage.
Scale a value against training bounds and report range excess.
When to use this tool
Use Minmax Feature Train Test Range Gap when checking one supplied feature-transform or encoding component.
How to use the tool and how it works
- Enter value, trainMinimum, trainMaximum. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read scaled, belowRange, aboveRange. Use the output convention below.
Scale a value against training bounds and report range excess. Require trainMaximum > trainMinimum; scaled = (value-min)/(max-min), with nonnegative belowRange/aboveRange in original units.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| value | Value | Required. Finite number. |
| trainMinimum | Train Minimum | Required. Finite number. |
| trainMaximum | Train Maximum | Required. Finite number. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| scaled | Scaled | Required. Finite number. |
| belowRange | Below Range | Required. Finite number. |
| aboveRange | Above Range | Required. Finite number. |
{
"input": {
"value": 12,
"trainMinimum": 2,
"trainMaximum": 10
},
"output": {
"scaled": 1.25,
"belowRange": 0,
"aboveRange": 2
}
}Limitations and assumptions
- Scaled output is not clipped; extrema are supplied rather than fitted only from training data.
- 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.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.
Alternative or Complementary methods
Use a correctly fitted preprocessing pipeline and suitable validation design for complete data transformation and model assessment.
References
Similar or alternative tools
- Sparse Feature One Entry Density
Return sparse-feature density before and after added nonzeros.
- Batch Drift One Feature Mean Gap
Compare a batch mean with a supplied reference mean and standard deviation.