Description
Return a minimum score of zero and a maximum of two, or 2/classes when normalized is true, requiring at least two classes.
Probabilistic scores evaluate predictions against outcomes. Probability-domain rules, logarithm bases, and whether class terms are summed or averaged must be explicit.
Return a minimum score of zero and a maximum of two, or 2/classes when normalized is true, requiring at least two classes.
When to use this tool
Use Multiclass Brier Single Observation Range when you need one probability-loss term or its declared range.
How to use the tool and how it works
- Enter classes, normalized. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read minimum, maximum. Use the output convention below.
Return a minimum score of zero and a maximum of two, or 2/classes when normalized is true, requiring at least two classes.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| classes | Classes | Required. Safe integer; minimum 2. |
| normalized | Normalized | Required. Boolean. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| minimum | Minimum | Required. Finite number. |
| maximum | Maximum | Required. Finite number. |
{
"input": {
"classes": 4,
"normalized": false
},
"output": {
"minimum": 0,
"maximum": 2
}
}Limitations and assumptions
- The range assumes a valid class-probability vector and one-hot outcome; neither is supplied or validated by this range-only tool.
- 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 complete set of validated predictions and examine calibration as well as aggregate score.
References
Similar or alternative tools
- Binary Log Loss Logit Stable Term
For binary outcome, calculate max(logit, 0) - outcome × logit + log1p(exp(-abs(logit))).
- Metrics: Log Loss Probability Term
Compute binary natural-log loss from probability and outcome, then multiply by nonnegative weight.