Description
Calculate one class's recall and contribution to balanced accuracy.
Classification terms depend on label vocabularies, decision thresholds and denominator definitions. One class recall is not a full balanced-accuracy metric, and a row or column total is not automatically a precision or recall denominator. Empty or zero-total cases need an explicit defined flag or convention.
Calculate one class's recall and contribution to balanced accuracy.
When to use this tool
Use Balanced Accuracy Single Class Term when you already have correctClassCount, actualClassCount, numberOfClasses and need to calculate one class's recall and contribution to balanced accuracy.
How to use the tool and how it works
- Enter correctClassCount, actualClassCount, numberOfClasses. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read classRecall, balancedAccuracyContribution. Use the output convention below.
Require positive safe actual-class count and number of classes, with correct count within actual; return correct/actual and that recall/numberOfClasses.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| correctClassCount | Correct Class Count | Required. Safe integer; minimum 0. |
| actualClassCount | Actual Class Count | Required. Safe integer; minimum 1. |
| numberOfClasses | Number Of Classes | Required. Safe integer; minimum 1. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| classRecall | Class Recall | Required. Finite number. |
| balancedAccuracyContribution | (correctClassCount/actualClassCount)/numberOfClasses; one class term rather than the full metric. | Required. Finite number. |
{
"input": {
"correctClassCount": 8,
"actualClassCount": 10,
"numberOfClasses": 4
},
"output": {
"classRecall": 0.8,
"balancedAccuracyContribution": 0.2
}
}Limitations and assumptions
- This is one class contribution, not complete balanced accuracy. No absent-class policy, confusion matrix, class set, actual predictions or model quality is verified.
- 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 prediction set, confusion structure and decision policy. Report missingness, class scope and uncertainty with the full metric; local threshold flags or one-cell fractions cannot characterize overall model performance.
References
Similar or alternative tools
- Confusion Cell Row Share Column Share
Calculate a supplied confusion cell's row and column shares.
- Selective Classification Accepted Count
Calculate supplied nonabstained prediction count and conditional coverage.