com.datumbox.framework.core.machinelearning.preprocessing

Class StandardScaler

java.lang.Object

com.datumbox.framework.core.machinelearning.common.abstracts.AbstractTrainer<MP,TP>

com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractTransformer<MP,TP>

com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractScaler<StandardScaler.ModelParameters,StandardScaler.TrainingParameters>

com.datumbox.framework.core.machinelearning.preprocessing.StandardScaler

All Implemented Interfaces:

Savable, Parallelizable, Trainable<StandardScaler.ModelParameters,StandardScaler.TrainingParameters>, java.lang.AutoCloseable

public class StandardScaler
extends AbstractScaler<StandardScaler.ModelParameters,StandardScaler.TrainingParameters>

Rescales the numerical features of the dataset by subtracting the mean and dividing by the standard deviation.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	StandardScaler.ModelParameters Base class for every ModelParameter class in the framework.
static class	StandardScaler.TrainingParameters The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.

Nested classes/interfaces inherited from class com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractScaler

AbstractScaler.AbstractModelParameters, AbstractScaler.AbstractTrainingParameters

Method Summary

Methods inherited from class com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractTransformer

fit_transform, isParallelized, setParallelized, transform

Methods inherited from class com.datumbox.framework.core.machinelearning.common.abstracts.AbstractTrainer

close, delete, fit, getModelParameters, getTrainingParameters, save

Methods inherited from class java.lang.Object

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