StandardScalerModel#

class pyspark.mllib.feature.StandardScalerModel(java_model)[source]#

Represents a StandardScaler model that can transform vectors.

Added in version 1.2.0.

Methods

`call`(name, *a)	Call method of java_model
`setWithMean`(withMean)	Setter of the boolean which decides whether it uses mean or not
`setWithStd`(withStd)	Setter of the boolean which decides whether it uses std or not
`transform`(vector)	Applies standardization transformation on a vector.

Attributes

`mean`	Return the column mean values.
`std`	Return the column standard deviation values.
`withMean`	Returns if the model centers the data before scaling.
`withStd`	Returns if the model scales the data to unit standard deviation.

Methods Documentation

setWithMean(withMean)[source]#: Setter of the boolean which decides whether it uses mean or not

Added in version 1.4.0.

setWithStd(withStd)[source]#: Setter of the boolean which decides whether it uses std or not

Added in version 1.4.0.

transform(vector)[source]#

Applies standardization transformation on a vector.

Added in version 1.2.0.

Parameters:

vectorpyspark.mllib.linalg.Vector or pyspark.RDD: Input vector(s) to be standardized.

Returns:

pyspark.mllib.linalg.Vector or pyspark.RDD: Standardized vector(s). If the variance of a column is zero, it will return default 0.0 for the column with zero variance.

Notes

In Python, transform cannot currently be used within an RDD transformation or action. Call transform directly on the RDD instead.

Attributes Documentation

withMean#: Returns if the model centers the data before scaling.

Added in version 2.0.0.

withStd#: Returns if the model scales the data to unit standard deviation.

Added in version 2.0.0.