Description
Return modelLoss / referenceLoss - 1 when higherIsBetter, otherwise 1 - modelLoss / referenceLoss.
A skill comparison expresses performance relative to a reference loss or scale. Zero baselines and orientation choices can make a nominal score undefined or reverse its interpretation.
Return modelLoss / referenceLoss - 1 when higherIsBetter, otherwise 1 - modelLoss / referenceLoss.
When to use this tool
Use Skill Score Sign Reference Choice when you need an explicit baseline-relative error or equality term.
How to use the tool and how it works
- Enter modelLoss, referenceLoss, higherIsBetter. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read skill. Use the output convention below.
Require nonzero referenceLoss. Return modelLoss / referenceLoss - 1 when higherIsBetter, otherwise 1 - modelLoss / referenceLoss.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| modelLoss | Model Loss | Required. Finite number. |
| referenceLoss | Reference Loss | Required. Finite number. |
| higherIsBetter | Higher Is Better | Required. Boolean. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| skill | Skill | Required. Finite number. |
{
"input": {
"modelLoss": 2,
"referenceLoss": 4,
"higherIsBetter": false
},
"output": {
"skill": 0.5
}
}Limitations and assumptions
- The meaning and sign of the supplied measure must fit the chosen orientation; negative reference values are accepted and can complicate interpretation.
- 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
Check baseline suitability and aggregate losses on comparable observations before interpreting relative performance.
References
Similar or alternative tools
- Relative Skill Zero Baseline Policy
For nonnegative loss and positive baseline return 1 - loss / baseline.
- Forecast Skill Baseline Equality Point
Subtract baselineLoss from modelLoss for residual.