object HashNHotEncoder extends SettingsBuilder with Serializable

Transform a collection of categorical features to binary columns, with at most N one-values. Similar to NHotEncoder but uses MurmursHash3 to hash features into buckets to reduce CPU and memory overhead.

Missing values are transformed to zero vectors.

If hashBucketSize is inferred with HLL, the estimate is scaled by sizeScalingFactor to reduce the number of collisions.

Rough table of relationship of scaling factor to % collisions, measured from a corpus of 466544 English words:

sizeScalingFactor     % Collisions
-----------------     ------------
                2     17.9934%
                4     10.5686%
                8      5.7236%
                16      3.0019%
                32      1.5313%
                64      0.7864%
              128      0.3920%
              256      0.1998%
              512      0.0975%
              1024      0.0478%
              2048      0.0236%
              4096      0.0071%
Source
HashNHotEncoder.scala
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  4. def apply(name: String, hashBucketSize: Int = 0, sizeScalingFactor: Double = 8.0): Transformer[Seq[String], HLL, Int]

    Create a new HashNHotEncoder instance.

    Create a new HashNHotEncoder instance.

    hashBucketSize

    number of buckets, or 0 to infer from data with HyperLogLog

    sizeScalingFactor

    when hashBucketSize is 0, scale HLL estimate by this amount

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  10. def fromSettings(setting: Settings): Transformer[Seq[String], HLL, Int]

    Create a new HashNHotEncoder from a settings object

    Create a new HashNHotEncoder from a settings object

    setting

    Settings object

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    HashNHotEncoderSettingsBuilder
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