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Prediction Reject Band Membership Tool

Compute prediction reject band membership from explicit bounded inputs.

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Description

Check whether a probability score belongs to a rejection band.

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.

Check whether a probability score belongs to a rejection band.

When to use this tool

Use Prediction Reject Band Membership when checking the stated score comparison, tie or decision-band policy.

How to use the tool and how it works

  1. Enter score, lower, upper, closed. Use the units and conventions below.
  2. Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
  3. Read reject. Inspect reject before interpreting the associated values.

Check whether a probability score belongs to a rejection band. Require score/lower/upper in [0,1] and lower <= upper; closed includes both endpoints, otherwise both comparisons are strict.1

Input fieldMeaning and unitsAccepted input
scoreScoreRequired. Finite number; minimum 0; maximum 1.
lowerLowerRequired. Finite number; minimum 0; maximum 1.
upperUpperRequired. Finite number; minimum 0; maximum 1.
closedClosedRequired. Boolean.
Output fieldInterpretationReturned shape
rejectRejectRequired. Boolean.
json
{
  "input": {
    "score": 0.4,
    "lower": 0.4,
    "upper": 0.6,
    "closed": true
  },
  "output": {
    "reject": true
  }
}

Limitations and assumptions

  • A coincident open band is empty; the rejection rule is supplied rather than learned or validated.
  • 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

  1. Calculation and input contract: classification/prediction-reject-band-membership — tool.io

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