org.apache.spark.mllib.feature

StandardScaler

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.

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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)

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

    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" )

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