Description
Return one expected confusion count under independence.
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 expected confusion count under independence.
When to use this tool
Use Confusion Cell Expected Independence Term when checking the stated confusion-count, metric contribution or abstention-denominator convention.
How to use the tool and how it works
- Enter rowTotal, columnTotal, total. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read expected. Use the output convention below.
Return one expected confusion count under independence. Require whole total >= 1 and row/column totals in [0,total]; expected = rowTotal*columnTotal/total.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| rowTotal | Row Total | Required. Safe integer; minimum 0. |
| columnTotal | Column Total | Required. Safe integer; minimum 0. |
| total | Total | Required. Safe integer; minimum 1. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| expected | Expected | Required. Finite number. |
{
"input": {
"rowTotal": 4,
"columnTotal": 5,
"total": 10
},
"output": {
"expected": 2
}
}Limitations and assumptions
- These supplied marginals are not a verified complete contingency table, hypothesis test or significance result.
- 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
- Confusion Row Diagonal Dominance Gap
Compare one confusion row's diagonal with all off-diagonal mass.
- Fbeta One Cell Sensitivity
Return local F-beta sensitivity to one confusion cell.