Description
Recover false positives from predicted-positive and true-positive counts.
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.
Recover false positives from predicted-positive and true-positive counts.
When to use this tool
Use Classwise False Discovery Count Balance when checking the stated confusion-count, metric contribution or abstention-denominator convention.
How to use the tool and how it works
- Enter predictedPositive, truePositive. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read falsePositive. Use the output convention below.
Recover false positives from predicted-positive and true-positive counts. Require whole predictedPositive >= truePositive >= 0; falsePositive = predictedPositive-truePositive.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| predictedPositive | Predicted Positive | Required. Safe integer; minimum 0. |
| truePositive | True Positive | Required. Safe integer; minimum 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| falsePositive | False Positive | Required. Safe integer. |
{
"input": {
"predictedPositive": 40,
"truePositive": 30
},
"output": {
"falsePositive": 10
}
}Limitations and assumptions
- Counts must describe the same population and positive label; no actual predictions or confusion-table consistency is checked.
- 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.