Description
Return zeroPolicy as the selected absolute-error subgradient at equality, requiring a value in [-1, 1].
Signed errors preserve direction while absolute or squared errors emphasize magnitude. Their gradients and small-residual approximations represent specific mathematical terms rather than complete performance evaluations.
Return zeroPolicy as the selected absolute-error subgradient at equality, requiring a value in [-1, 1].
When to use this tool
Use Mae Subgradient Zero Policy when you need an explicit error, gradient, or local loss component.
How to use the tool and how it works
- Enter zeroPolicy. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read subgradient. Use the output convention below.
Return zeroPolicy as the selected absolute-error subgradient at equality, requiring a value in [-1, 1].1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| zeroPolicy | Zero Policy | Required. Finite number. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| subgradient | Subgradient | Required. Finite number. |
{
"input": {
"zeroPolicy": 0
},
"output": {
"subgradient": 0
}
}Limitations and assumptions
- This is the equality case only; it does not inspect actual or predicted values or evaluate a nonzero-residual gradient.
- 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
Assess a full error distribution and keep the sign and aggregation conventions consistent across models.
References
Similar or alternative tools
- Mae Single Observation Gradient
Require actual unequal to predicted and return sign(predicted - actual) as the gradient with respect to predicted.
- Mse Single Observation Gradient
Compute 2 × weight × (predicted - actual) / denominator, with nonnegative weight and positive denominator.