Class

org.apache.spark.mllib.feature

StandardScaler

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class StandardScaler extends Logging

Standardizes features by removing the mean and scaling to unit std using column summary statistics on the samples in the training set.

The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.

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@Since( "1.1.0" )
Source
StandardScaler.scala
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Instance Constructors

  1. new StandardScaler()

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    @Since( "1.1.0" )

  2. new StandardScaler(withMean: Boolean, withStd: Boolean)

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    withMean

    False by default. Centers the data with mean before scaling. It will build a dense output, so this does not work on sparse input and will raise an exception.

    withStd

    True by default. Scales the data to unit standard deviation.

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    @Since( "1.1.0" )

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  1. final def !=(arg0: Any): Boolean

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  9. def fit(data: RDD[Vector]): StandardScalerModel

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    Computes the mean and variance and stores as a model to be used for later scaling.

    Computes the mean and variance and stores as a model to be used for later scaling.

    data

    The data used to compute the mean and variance to build the transformation model.

    returns

    a StandardScalarModel

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    @Since( "1.1.0" )

  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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