Package smile.feature.transform

Class Standardizer

java.lang.Object

smile.feature.transform.Standardizer

public class Standardizer
extends Object

Standardizes numeric feature to 0 mean and unit variance. Standardization makes an assumption that the data follows a Gaussian distribution and are also not robust when outliers present. A robust alternative is to subtract the median and divide by the IQR by RobustStandardizer.

Constructor Summary

Constructors

Constructor	Description
Standardizer()

Method Summary

Modifier and Type	Method	Description
static InvertibleColumnTransform	fit(DataFrame data, String... columns)	Fits the data transformation.

Methods inherited from class java.lang.Object

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

Constructor Details

Standardizer

public Standardizer()

Method Details

fit

public static InvertibleColumnTransform fit(DataFrame data,
 String... columns)

Fits the data transformation.

Parameters:

data - the training data.

columns - the columns to transform. If empty, transform all the numeric columns.

Returns:

the transform.