Description
Return a binary Brier uncertainty contribution.
Classifier metrics count particular outcomes on a declared population and positive-label convention. Precision, recall and accepted-only measures use different denominators. A reporting value at an empty denominator is a policy choice, while one cell, derivative or contribution does not constitute a complete performance evaluation.
Return a binary Brier uncertainty contribution.
When to use this tool
Use Brier Uncertainty Single Class Term when checking the stated confusion-count, metric contribution or abstention-denominator convention.
How to use the tool and how it works
- Enter prevalence. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read contribution. Use the output convention below.
Return a binary Brier uncertainty contribution. Require prevalence in [0,1]; contribution = prevalence*(1-prevalence).1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| prevalence | Prevalence | Required. Finite number; minimum 0; maximum 1. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| contribution | prevalence*(1-prevalence); one binary Brier uncertainty component. | Required. Finite number. |
{
"input": {
"prevalence": 0.25
},
"output": {
"contribution": 0.1875
}
}Limitations and assumptions
- This is one uncertainty term, not a full Brier score, decomposition or evaluation of prediction calibration.
- 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
Evaluate the complete confusion table, relevant class supports and abstention coverage together; use suitable uncertainty and validation methods for conclusions about predictive performance.
References
Similar or alternative tools
- Class Recall Minimum Count Margin
Return additional true positives needed for a recall target at fixed support.
- Binary Specificity Minimum Count Margin
Return additional true negatives needed for a specificity target.