Description
Return maximal-logit indexes under a tie policy.
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 maximal-logit indexes under a tie policy.
When to use this tool
Use Classifier Logit Tie Policy when checking the stated score comparison, tie or decision-band policy.
How to use the tool and how it works
- Enter logits, policy. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read indices. Use the output convention below.
Return maximal-logit indexes under a tie policy. Require nonempty logits and policy all/first/last; find exact maxima and return all tied positions or the first/last one as an array.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| logits | Logits | Required. Array of finite number; maximum items 256. |
| policy | Policy | Required. Text; one of "all", "first", "last"; maximum UTF-16 length 8192. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| indices | Indices | Required. Array of safe integer; maximum items 256. |
{
"input": {
"logits": [
1,
3,
3
],
"policy": "all"
},
"output": {
"indices": [
1,
2
]
}
}Limitations and assumptions
- No softmax, sampling, tolerance or confidence estimate is applied; indexes preserve input ordering.
- 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
- Class Label Cost Tie Selector
Select a minimum-cost class under an explicit tie policy.
- Classification: Binary Probability Rounding Tie
Round one binary probability with an explicit half tie.