Description
Return one class's balanced-error 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 one class's balanced-error contribution.
When to use this tool
Use Binary Balanced Error Contribution when checking the stated confusion-count, metric contribution or abstention-denominator convention.
How to use the tool and how it works
- Enter errors, classTotal. 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 one class's balanced-error contribution. Require whole classTotal >= 1 and errors within it; contribution = 0.5*errors/classTotal.1
Classifier metrics use different outcome supports and denominator conventions. A zero-denominator reporting policy does not supply missing observed outcomes.2
| Input field | Meaning and units | Accepted input |
|---|---|---|
| errors | Errors | Required. Safe integer; minimum 0. |
| classTotal | Class Total | Required. Safe integer; minimum 1. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| contribution | Contribution | Required. Finite number. |
{
"input": {
"errors": 10,
"classTotal": 50
},
"output": {
"contribution": 0.1
}
}Limitations and assumptions
- This is one half-weighted class term, not overall binary balanced error without the other class contribution.
- 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
-
Calculation and input contract: classification/binary-balanced-error-contribution — tool.io
-
Model evaluation: classification metrics — scikit-learn developers
Similar or alternative tools
- Classification: Binary Confusion Positive Label Swap
Relabel a binary confusion table's positive class.
- Binary Abstention Coverage Count
Return acceptance coverage from positive, negative and abstained counts.