org.apache.spark.mllib.feature

Class StandardScaler

Object

org.apache.spark.mllib.feature.StandardScaler

All Implemented Interfaces:

Logging

public class StandardScaler
extends Object
implements 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 (https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation), which is computed as the square root of the unbiased sample variance.

param: withMean False by default. Centers the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. param: withStd True by default. Scales the data to unit standard deviation.

Constructor Summary

Constructors

Constructor and Description
StandardScaler()
StandardScaler(boolean withMean, boolean withStd)

Method Summary

Modifier and Type	Method and Description
StandardScalerModel	fit(RDD<Vector> data) Computes the mean and variance and stores as a model to be used for later scaling.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

StandardScaler

public StandardScaler(boolean withMean,
                      boolean withStd)

StandardScaler

public StandardScaler()

Method Detail

fit

public StandardScalerModel fit(RDD<Vector> data)

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

Parameters:

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

Returns:

a StandardScalarModel