Package smile.feature.transform

Class WinsorScaler

java.lang.Object

smile.feature.transform.WinsorScaler

public class WinsorScaler
extends Object

Scales all numeric variables into the range [0, 1]. If the dataset has outliers, normalization will certainly scale the "normal" data to a very small interval. In this case, the Winsorization procedure should be applied: values greater than the specified upper limit are replaced with the upper limit, and those below the lower limit are replaced with the lower limit. Often, the specified range is indicate in terms of percentiles of the original distribution (like the 5th and 95th percentile).

Constructor Summary

Constructors

Constructor	Description
WinsorScaler()

Method Summary

Modifier and Type	Method	Description
static InvertibleColumnTransform	fit(DataFrame data)	Fits the data transformation with 5% lower limit and 95% upper limit.
static InvertibleColumnTransform	fit(DataFrame data, double lower, double upper, 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

WinsorScaler

public WinsorScaler()

Method Details

fit

public static InvertibleColumnTransform fit(DataFrame data)

Fits the data transformation with 5% lower limit and 95% upper limit.

Parameters:

data - the training data.

Returns:

the transform.

fit

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

Fits the data transformation.

Parameters:

data - the training data.

lower - the lower limit in terms of percentiles of the original distribution (e.g. 5th percentile).

upper - the upper limit in terms of percentiles of the original distribution (e.g. 95th percentile).

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

Returns:

the transform.