Description
Return ordinal class error cost.
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 ordinal class error cost.
When to use this tool
Use Ordinal Label Cost Matrix Cell when evaluating the stated ordered-label target, distance or mass convention.
How to use the tool and how it works
- Enter actual, predicted, classes, squared. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read cost. Use the output convention below.
Return ordinal class error cost. Require classes >= 2 and valid actual/predicted indexes; d = abs(actual-predicted), cost = d² when squared, otherwise d.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| actual | Actual | Required. Safe integer; minimum 0. |
| predicted | Predicted | Required. Safe integer; minimum 0. |
| classes | Classes | Required. Safe integer; minimum 2; maximum 1000000. |
| squared | Squared | Required. Boolean. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| cost | Cost | Required. Finite number. |
{
"input": {
"actual": 1,
"predicted": 4,
"classes": 5,
"squared": true
},
"output": {
"cost": 9
}
}Limitations and assumptions
- This assumes index-based spacing/cost and does not validate meaningful distances between real class labels.
- 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 Cumulative Label Target
Return cumulative ordinal targets from one class index.
- Ordinal Median Class Boundary
Select a weighted ordinal median under an equality policy.