Description
Calculate the midpoint of supplied ordered quantization-bin edges.
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.
Calculate the midpoint of supplied ordered quantization-bin edges.
When to use this tool
Use Feature Quantization Single Bin Midpoint when you already have lowerEdge, upperEdge and need to calculate the midpoint of supplied ordered quantization-bin edges.
How to use the tool and how it works
- Enter lowerEdge, upperEdge. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read midpoint. Use the output convention below.
Require upperEdge>=lowerEdge; return lower/2+upper/2.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| lowerEdge | Lower Edge | Required. Finite number. |
| upperEdge | Upper Edge | Required. Finite number. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| midpoint | Midpoint | Required. Finite number. |
{
"input": {
"lowerEdge": 2,
"upperEdge": 6
},
"output": {
"midpoint": 4
}
}Limitations and assumptions
- A zero-width bin is accepted. This does not quantize observations, define endpoint membership or estimate a within-bin distribution or reconstruction error.
- 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
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
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