Description
Return a target label's mass under uniform top-score tie selection.
Classification scores rank labels or locate a prediction relative to a chosen decision boundary. Raw scores and logits need not be calibrated probabilities; exact ties, score direction and inclusion of boundary values are separate policy choices that can change the reported decision.
Return a target label's mass under uniform top-score tie selection.
When to use this tool
Use Prediction Tie Random Mass Share when checking the stated score comparison, tie or decision-band policy.
How to use the tool and how it works
- Enter scores, targetIndex. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read tieCount, targetProbability. Use the output convention below.
Return a target label's mass under uniform top-score tie selection. Require nonempty scores and valid targetIndex; tieCount counts exact maxima, targetProbability is 1/tieCount for a maximum, otherwise zero.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| scores | Scores | Required. Array of finite number; maximum items 256. |
| targetIndex | Target Index | Required. Safe integer; minimum 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| tieCount | Tie Count | Required. Safe integer. |
| targetProbability | Uniform exact-top-tie selection mass for one target index, not an actual sampled prediction. | Required. Finite number. |
{
"input": {
"scores": [
1,
2,
2
],
"targetIndex": 1
},
"output": {
"tieCount": 2,
"targetProbability": 0.5
}
}Limitations and assumptions
- No random prediction is generated and scores need not be calibrated probabilities; equality uses no tolerance.
- 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
Use a complete evaluation dataset and an explicit decision policy to assess performance; fit and validate calibration separately when a probability interpretation is required.
References
Similar or alternative tools
- Top K Tie Slot Budget
Return remaining top-k slots within a score tie.
- Classifier Logit Tie Policy
Return maximal-logit indexes under a tie policy.