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Feature Hash Signed Bucket Term Tool

Supplied nonnegative hash uses modulo for bucket and parity of the quotient for sign; no hash algorithm is selected.

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Description

Return a signed feature contribution from a supplied hash.

Weights, hash collisions and regression diagnostics have estimator-specific meanings. A local count, signed contribution or residual scaling can be calculated from supplied inputs without establishing sampling validity or model quality.

Return a signed feature contribution from a supplied hash.

When to use this tool

Use Feature Hash Signed Bucket Term when checking the stated weight, hash or diagnostic contribution.

How to use the tool and how it works

  1. Enter hash, buckets, value. 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 bucket, signedValue. Use the output convention below.

Return a signed feature contribution from a supplied hash. Require nonnegative whole hash and positive whole buckets; bucket is exact integer remainder and quotient parity selects positive or negative value.1

Input fieldMeaning and unitsAccepted input
hashSupplied safe integer; the tool does not compute a hash.Required. Safe integer; minimum 0.
bucketsBucketsRequired. Safe integer; minimum 1.
valueValueRequired. Finite number.
Output fieldInterpretationReturned shape
bucketBucketRequired. Safe integer.
signedValuevalue with sign chosen from the quotient parity under the documented bucket rule.Required. Finite number.
json
{
  "input": {
    "hash": 13,
    "buckets": 4,
    "value": 2
  },
  "output": {
    "bucket": 1,
    "signedValue": -2
  }
}

Limitations and assumptions

  • No hash function is run and no independence, uniformity or collision-free mapping is guaranteed.
  • 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 estimator or feature pipeline and justified assumptions to assess statistical impact, uncertainty and model behavior.

References

  1. Calculation and input contract: data-science/feature-hash-signed-bucket-term — tool.io

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