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Quantile Loss Zero Residual Subgradient Tool

Return the pinball-loss subgradient interval with respect to the residual at exactly zero.

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Description

For quantile in [0, 1], return the interval [quantile - 1, quantile] of residual subgradients at zero residual.

Pinball loss weights underprediction and overprediction differently for a target quantile. Its kink at zero residual requires a subgradient convention, and derivative signs depend on the variable.

For quantile in [0, 1], return the interval [quantile - 1, quantile] of residual subgradients at zero residual.

When to use this tool

Use Quantile Loss Zero Residual Subgradient when you need an explicit quantile-loss term or convention.

How to use the tool and how it works

  1. Enter quantile. 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 lower, upper. Use the output convention below.

For quantile in [0, 1], return the interval [quantile - 1, quantile] of residual subgradients at zero residual.1

Input fieldMeaning and unitsAccepted input
quantileQuantile level as a fraction, not a percentage.Required. Finite number; minimum 0; maximum 1.
Output fieldInterpretationReturned shape
lowerLower residual-subgradient endpoint, quantile - 1.Required. Finite number.
upperUpper residual-subgradient endpoint, quantile.Required. Finite number.
json
{
  "input": {
    "quantile": 0.25
  },
  "output": {
    "lower": -0.75,
    "upper": 0.25
  }
}

Limitations and assumptions

  • The derivative is with respect to residual actual minus prediction; prediction subgradients have reversed signs.
  • 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

Keep residual definitions consistent and assess quantile calibration and crossing across a complete forecast set.

References

  1. Calculation and input contract: metrics/quantile-loss-zero-residual-subgradient — tool.io

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