Description
Return correct-probability indifference between classification and abstention.
A classification decision can compare expected losses or utilities under supplied probabilities and outcome costs. The resulting threshold depends on which outcomes are charged, the cost signs and whether abstention is allowed; arithmetic alone does not establish appropriate costs or reliable probabilities.
Return correct-probability indifference between classification and abstention.
When to use this tool
Use Abstention Cost Indifference Threshold when comparing the stated classification, rejection or utility terms under supplied assumptions.
How to use the tool and how it works
- Enter wrongClassificationCost, abstentionCost, correctClassificationCost. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read correctProbabilityThreshold, withinUnitInterval. Inspect withinUnitInterval before interpreting the associated values.
Return correct-probability indifference between classification and abstention. Require wrongClassificationCost != correctClassificationCost; threshold = (wrong-abstention)/(wrong-correct), and flag whether it lies in [0,1].1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| wrongClassificationCost | Wrong Classification Cost | Required. Finite number. |
| abstentionCost | Abstention Cost | Required. Finite number. |
| correctClassificationCost | Correct Classification Cost | Required. Finite number. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| correctProbabilityThreshold | Correct Probability Threshold | Required. Finite number. |
| withinUnitInterval | Whether the returned threshold is in [0,1]; a false result is not an input error. | Required. Boolean. |
{
"input": {
"wrongClassificationCost": 10,
"abstentionCost": 2,
"correctClassificationCost": 0
},
"output": {
"correctProbabilityThreshold": 0.8,
"withinUnitInterval": true
}
}Limitations and assumptions
- Signed costs and thresholds outside [0,1] are returned; no actual optimal decision or plausible utility model is established.
- 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
Specify the full decision problem and validate probabilities and outcome costs before using a threshold; assess performance and consequences on relevant data.
References
Similar or alternative tools
- Binary Rejection Cost Indifference
Return expected positive, negative and reject risks.
- Classification: Decision Threshold To Cost Ratio
Return a Bayes probability threshold and cost ratio.