Description
Return predicted-positive count and prevalence.
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 predicted-positive count and prevalence.
When to use this tool
Use Binary Confusion Predicted Prevalence Term when checking the stated confusion-count, metric contribution or abstention-denominator convention.
How to use the tool and how it works
- Enter truePositive, falsePositive, total. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read predictedPositive, prevalence. Use the output convention below.
Return predicted-positive count and prevalence. Require nonnegative whole TP/FP and positive total with TP+FP <= total; predictedPositive sums them, prevalence divides by total.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| truePositive | True Positive | Required. Safe integer; minimum 0. |
| falsePositive | False Positive | Required. Safe integer; minimum 0. |
| total | Total | Required. Safe integer; minimum 1. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| predictedPositive | Predicted Positive | Required. Safe integer. |
| prevalence | Predicted-positive count divided by total; not actual positive-label prevalence. | Required. Finite number. |
{
"input": {
"truePositive": 30,
"falsePositive": 10,
"total": 100
},
"output": {
"predictedPositive": 40,
"prevalence": 0.4
}
}Limitations and assumptions
- The prevalence is predicted rather than actual; no complete confusion matrix or population prevalence is estimated.
- 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
- Binary Confusion Transpose Role Change
Transpose the roles of binary confusion axes.
- Classification: Binary Confusion Positive Label Swap
Relabel a binary confusion table's positive class.