Description
Return nonnegative score mass and target deficit.
Class prevalence, probability mass and training weights describe different distributions. Removing a class, changing class weights or correcting prior odds changes a particular numerical basis; none of those operations by itself estimates population shift or establishes calibrated predictions.
Return nonnegative score mass and target deficit.
When to use this tool
Use Multiclass Score Mass Deficit when checking the stated probability-mass, prior-odds or weighting convention.
How to use the tool and how it works
- Enter scores, targetMass. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read mass, deficit. Use the output convention below.
Return nonnegative score mass and target deficit. Require targetMass >= 0 and all scores >= 0; mass = sum(scores), deficit = targetMass-mass.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| scores | Scores | Required. Array of finite number; minimum 0; maximum items 256. |
| targetMass | Target Mass | Required. Finite number; minimum 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| mass | Mass | Required. Finite number. |
| deficit | Deficit | Required. Finite number. |
{
"input": {
"scores": [
0.2,
0.3
],
"targetMass": 1
},
"output": {
"mass": 0.5,
"deficit": 0.5
}
}Limitations and assumptions
- A negative deficit reports excess mass; no probability normalization or calibrated classifier output is established.
- 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
Check the complete class distribution and its sampling basis; use a justified shift model or fitted calibration method when adapting predictions to a new population.
References
Similar or alternative tools
- Predicted Prior Mass Residual
Compare predicted and supplied prior class fractions.
- Classifier Probability Sum Roundoff Gap
Compare total class probability mass with one.