Class Label Cost Tie Selector Tool

Description

Select a minimum-cost class under an explicit 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.

Select a minimum-cost class under an explicit tie policy.

When to use this tool

Use Class Label Cost Tie Selector when checking the stated score comparison, tie or decision-band policy.

How to use the tool and how it works

Enter labels, costs, tiePolicy. Follow any array-alignment requirements below. Use the units and conventions below.
Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
Read selected, selectedIndex, tiedLabels. Use the output convention below.

Select a minimum-cost class under an explicit tie policy. Require aligned unique labels/costs, nonempty labels and first/last policy; collect exact minimum-cost labels and choose the corresponding endpoint index.^[1]

Input field	Meaning and units	Accepted input
labels	Labels	Required. Array of text; maximum UTF-16 length 8192; maximum items 256.
costs	Costs	Required. Array of finite number; maximum items 256.
tiePolicy	Tie Policy	Required. Text; maximum UTF-16 length 8192.

Output field	Interpretation	Returned shape
selected	Selected	Required. Text; maximum UTF-16 length 8192.
selectedIndex	Selected Index	Required. Safe integer.
tiedLabels	Tied Labels	Required. Array of text; maximum UTF-16 length 8192; maximum items 256.

json

{
  "input": {
    "labels": [
      "a",
      "b",
      "c"
    ],
    "costs": [
      2,
      1,
      1
    ],
    "tiePolicy": "first"
  },
  "output": {
    "selected": "b",
    "selectedIndex": 1,
    "tiedLabels": [
      "b",
      "c"
    ]
  }
}

Limitations and assumptions

Costs may be signed and are supplied; no probability, utility model or meaningful label order is inferred.
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

Calculation and input contract: classification/class-label-cost-tie-selector — tool.io

Similar or alternative tools

Classifier Logit Tie Policy
Return maximal-logit indexes under a tie policy.
Classification: Binary Probability Rounding Tie
Round one binary probability with an explicit half tie.