Package org.deidentifier.arx.aggregates

Klasse ClassificationConfigurationRandomForest

java.lang.Object

org.deidentifier.arx.ARXClassificationConfiguration<ClassificationConfigurationRandomForest>

org.deidentifier.arx.aggregates.ClassificationConfigurationRandomForest

Alle implementierten Schnittstellen:

Serializable, Cloneable

public class ClassificationConfigurationRandomForest
extends ARXClassificationConfiguration<ClassificationConfigurationRandomForest>
implements Serializable, Cloneable

Configuration for Random Forest classifiers

Siehe auch:

• Serialisierte Form

Verschachtelte Klassen - Übersicht

Verschachtelte Klassen

Modifizierer und Typ	Klasse	Beschreibung
static enum	ClassificationConfigurationRandomForest.SplitRule	Split rule for the decision tree

Feldübersicht

Felder

Modifizierer und Typ	Feld	Beschreibung
static final int	DEFAULT_MAXMIMUM_NUMBER_OF_LEAF_NODES	Default value
static final int	DEFAULT_MINIMUM_SIZE_OF_LEAF_NODES	Default value
static final int	DEFAULT_NUMBER_OF_TREES	Default value
static final int	DEFAULT_NUMBER_OF_VARIABLES_TO_SPLIT	Default value = 0: sqrt(#features) seems to provide good results
static final ClassificationConfigurationRandomForest.SplitRule	DEFAULT_SPLIT_RULE	Split rule
static final double	DEFAULT_SUBSAMPLE	1.0 = sampling with replacement; Ungültige Eingabe: "<"1.0 = sampling without replacement

Von Klasse geerbte Felder org.deidentifier.arx.ARXClassificationConfiguration

DEFAULT_DETERMINISTIC, DEFAULT_MAX_RECORDS, DEFAULT_NUMBER_OF_FOLDS, DEFAULT_VECTOR_LENGTH

Methodenübersicht

Modifizierer und Typ	Methode	Beschreibung
ClassificationConfigurationRandomForest	clone()
static ClassificationConfigurationRandomForest	create()	Returns a new instance
int	getMaximumNumberOfLeafNodes()
int	getMinimumSizeOfLeafNodes()
int	getNumberOfTrees()
int	getNumberOfVariablesToSplit()
ClassificationConfigurationRandomForest.SplitRule	getSplitRule()
double	getSubsample()
void	parse(ARXClassificationConfiguration<?> config)	Parses another configuration
ClassificationConfigurationRandomForest	setMaximumNumberOfLeafNodes(int maximumNumberOfLeafNodes)
ClassificationConfigurationRandomForest	setMinimumSizeOfLeafNodes(int minimumSizeOfLeafNodes)
ClassificationConfigurationRandomForest	setNumberOfTrees(int numberOfTrees)
ClassificationConfigurationRandomForest	setNumberOfVariablesToSplit(int numberOfVariablesToSplit)
ClassificationConfigurationRandomForest	setSplitRule(ClassificationConfigurationRandomForest.SplitRule splitRule)
ClassificationConfigurationRandomForest	setSubsample(double subsample)

Von Klasse geerbte Methoden org.deidentifier.arx.ARXClassificationConfiguration

createLogisticRegression, createNaiveBayes, createRandomForest, getMaxRecords, getNumFolds, getSeed, getVectorLength, isDeterministic, isModified, setDeterministic, setMaxRecords, setModified, setNumFolds, setSeed, setUnmodified, setVectorLength

Von Klasse geerbte Methoden java.lang.Object

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

Felddetails

DEFAULT_NUMBER_OF_TREES

public static final int DEFAULT_NUMBER_OF_TREES

Default value

Siehe auch:

• Konstantenfeldwerte

DEFAULT_NUMBER_OF_VARIABLES_TO_SPLIT

public static final int DEFAULT_NUMBER_OF_VARIABLES_TO_SPLIT

Default value = 0: sqrt(#features) seems to provide good results

Siehe auch:

• Konstantenfeldwerte

DEFAULT_MINIMUM_SIZE_OF_LEAF_NODES

public static final int DEFAULT_MINIMUM_SIZE_OF_LEAF_NODES

Default value

Siehe auch:

• Konstantenfeldwerte

DEFAULT_MAXMIMUM_NUMBER_OF_LEAF_NODES

public static final int DEFAULT_MAXMIMUM_NUMBER_OF_LEAF_NODES

Default value

Siehe auch:

• Konstantenfeldwerte

DEFAULT_SUBSAMPLE

public static final double DEFAULT_SUBSAMPLE

1.0 = sampling with replacement; Ungültige Eingabe: "<"1.0 = sampling without replacement

Siehe auch:

• Konstantenfeldwerte

DEFAULT_SPLIT_RULE

public static final ClassificationConfigurationRandomForest.SplitRule DEFAULT_SPLIT_RULE

Split rule

Methodendetails

create

public static ClassificationConfigurationRandomForest create()

Returns a new instance

Gibt zurück:

clone

public ClassificationConfigurationRandomForest clone()

Angegeben von:

clone in Klasse ARXClassificationConfiguration<ClassificationConfigurationRandomForest>

getMaximumNumberOfLeafNodes

public int getMaximumNumberOfLeafNodes()

Gibt zurück:

the maximumNumberOfLeafNodes

getMinimumSizeOfLeafNodes

public int getMinimumSizeOfLeafNodes()

Gibt zurück:

the minimumSizeOfLeafNodes

getNumberOfTrees

public int getNumberOfTrees()

Gibt zurück:

the numberOfTrees

getNumberOfVariablesToSplit

public int getNumberOfVariablesToSplit()

Gibt zurück:

the numberOfVariablesToSplit

getSplitRule

public ClassificationConfigurationRandomForest.SplitRule getSplitRule()

Gibt zurück:

the splitRule

getSubsample

public double getSubsample()

Gibt zurück:

the subsample

parse

public void parse(ARXClassificationConfiguration<?> config)

Beschreibung aus Klasse kopiert: ARXClassificationConfiguration

Parses another configuration

Setzt außer Kraft:

parse in Klasse ARXClassificationConfiguration<ClassificationConfigurationRandomForest>

Parameter:

config -

setMaximumNumberOfLeafNodes

public ClassificationConfigurationRandomForest setMaximumNumberOfLeafNodes(int maximumNumberOfLeafNodes)

Parameter:

maximumNumberOfLeafNodes - the maximumNumberOfLeafNodes to set

setMinimumSizeOfLeafNodes

public ClassificationConfigurationRandomForest setMinimumSizeOfLeafNodes(int minimumSizeOfLeafNodes)

Parameter:

minimumSizeOfLeafNodes - the minimumSizeOfLeafNodes to set

setNumberOfTrees

public ClassificationConfigurationRandomForest setNumberOfTrees(int numberOfTrees)

Parameter:

numberOfTrees - the numberOfTrees to set

setNumberOfVariablesToSplit

public ClassificationConfigurationRandomForest setNumberOfVariablesToSplit(int numberOfVariablesToSplit)

Parameter:

numberOfVariablesToSplit - the numberOfVariablesToSplit to set

setSplitRule

public ClassificationConfigurationRandomForest setSplitRule(ClassificationConfigurationRandomForest.SplitRule splitRule)

Parameter:

splitRule - the splitRule to set

setSubsample

public ClassificationConfigurationRandomForest setSubsample(double subsample)

Parameter:

subsample - the subsample to set