o

com.spotify.featran.transformers

QuantileDiscretizer

object QuantileDiscretizer extends SettingsBuilder with Serializable

Transform a column of continuous features to n columns of binned categorical features. The number of bins is set by the numBuckets parameter.

The bin ranges are chosen using the Algebird's QTree approximate data structure. The precision of the approximation can be controlled with the k parameter.

Missing values are transformed to zero vectors.

When using aggregated feature summary from a previous session, values outside of previously seen [min, max] are binned into the first or last bucket and FeatureRejection.OutOfBound rejections are reported.

Source
QuantileDiscretizer.scala
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  4. def apply(name: String, numBuckets: Int = 2, k: Int = QTreeAggregator.DefaultK): Transformer[Double, B, C]

    Create a new QuantileDiscretizer instance.

    Create a new QuantileDiscretizer instance.

    numBuckets

    number of buckets (quantiles, or categories) into which data points are grouped, must be greater than or equal to 2

    k

    precision of the underlying Algebird QTree approximation

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  10. def fromSettings(setting: Settings): Transformer[Double, B, C]

    Create a new QuantileDiscretizer from a settings object

    Create a new QuantileDiscretizer from a settings object

    setting

    Settings object

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    QuantileDiscretizer → SettingsBuilder
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