Description
Calculate one level's prior-smoothed target-encoding term.
Data workflows require explicit training scope, feature/label availability, missingness codes, group assignments and evaluation direction. Supplied training statistics are not proof that preprocessing avoided leakage. A local target-encoding term or retransformed prediction depends on assumptions that must be checked in the complete model and validation design.
Calculate one level's prior-smoothed target-encoding term.
When to use this tool
Use Target Encoding Prior Single Level Term when you already have levelTargetSum, levelCount, priorMean, priorWeight and need to calculate one level's prior-smoothed target-encoding term.
How to use the tool and how it works
- Enter levelTargetSum, levelCount, priorMean, priorWeight. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read smoothedEncoding. Use the output convention below.
Require levelCount a nonnegative safe integer, priorWeight>=0 and count+weight>0; return (levelTargetSum+priorWeight*priorMean)/(levelCount+priorWeight).1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| levelTargetSum | Level Target Sum | Required. Finite number. |
| levelCount | Level Count | Required. Safe integer; minimum 0. |
| priorMean | Prior Mean | Required. Finite number. |
| priorWeight | Nonnegative smoothing weight on priorMean; count+weight must be positive. | Required. Finite number; minimum 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| smoothedEncoding | Smoothed Encoding | Required. Finite number. |
{
"input": {
"levelTargetSum": 12,
"levelCount": 4,
"priorMean": 2,
"priorWeight": 2
},
"output": {
"smoothedEncoding": 2.6666666666666665
}
}Limitations and assumptions
- Target sum and prior mean may be signed. No target type, valid prior, out-of-fold calculation, leakage prevention or model 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
Review the full data lineage, preprocessing fit, split design and evaluation procedure for model conclusions. Estimate required statistics only within the intended training folds and verify actual availability; local supplied-value transformations cannot establish those properties by themselves.
References
Similar or alternative tools
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