Description
Return cumulative ordinal targets from one class index.
Ordinal labels have an order, but adjacent categories need not be equally spaced measurements. Index distances, cumulative targets and weighted medians preserve a specified ordering while probability normalization, equality at a median boundary and distance costs remain distinct conventions.
Return cumulative ordinal targets from one class index.
When to use this tool
Use Ordinal Cumulative Label Target when evaluating the stated ordered-label target, distance or mass convention.
How to use the tool and how it works
- Enter labelIndex, classCount. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read targets. Use the output convention below.
Return cumulative ordinal targets from one class index. Require classCount 2..256 and labelIndex in [0,K); targets[i] = labelIndex > i for K-1 cut points.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| labelIndex | Label Index | Required. Safe integer; minimum 0. |
| classCount | Class Count | Required. Safe integer; minimum 2; maximum 256. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| targets | Targets | Required. Array of boolean; maximum items 256. |
{
"input": {
"labelIndex": 2,
"classCount": 4
},
"output": {
"targets": [
true,
true,
false
]
}
}Limitations and assumptions
- This encodes zero-based ordered labels without fitting a cumulative-link classifier or selecting meaningful class thresholds.
- 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 meaningful ordered labels and an appropriate ordinal model; evaluate prediction quality with costs and probability assumptions justified for the actual categories.
References
Similar or alternative tools
- Ordinal Label Cost Matrix Cell
Return ordinal class error cost.
- Ordinal Median Class Boundary
Select a weighted ordinal median under an equality policy.