Description
Return an odds multiplier for a supplied binary prior change.
Class prevalence, probability mass and training weights describe different distributions. Removing a class, changing class weights or correcting prior odds changes a particular numerical basis; none of those operations by itself estimates population shift or establishes calibrated predictions.
Return an odds multiplier for a supplied binary prior change.
When to use this tool
Use Classification: Label Prior Correction Odds Factor when checking the stated probability-mass, prior-odds or weighting convention.
How to use the tool and how it works
- Enter trainingPrior, targetPrior. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read result. Expand result to inspect the returned fields.
Return an odds multiplier for a supplied binary prior change. Require trainingPrior/targetPrior strictly in (0,1); posteriorOddsFactor = target odds/training odds.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| trainingPrior | Training Prior | Required. Finite number. |
| targetPrior | Target Prior | Required. Finite number. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| result | Returned calculated value or nested object under the stated output convention. | Required. Object. |
| result.posteriorOddsFactor | Target prior odds divided by training prior odds, under a compatible label-shift model. | Calculated result member: number. |
{
"input": {
"trainingPrior": 0.5,
"targetPrior": 0.2
},
"output": {
"result": {
"posteriorOddsFactor": 0.25
}
}
}Limitations and assumptions
- Use requires compatible label-shift assumptions and probability meaning; the tool does not infer shift or update a complete posterior itself.
- Outputs are nested inside 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
Check the complete class distribution and its sampling basis; use a justified shift model or fitted calibration method when adapting predictions to a new population.
References
Similar or alternative tools
- Binary Prior Shift Logit Offset
Return the logit offset from a supplied binary prior shift.
- Classification: Binary Probability Rounding Tie Tool
Round one binary probability to a class using an explicit half-probability tie policy; does not infer calibration.