public class ARFHoeffdingTree extends HoeffdingTree
Adaptive Random Forest Hoeffding Tree. This is the base model for the Adaptive Random Forest ensemble learner (See moa.classifiers.meta.AdaptiveRandomForest.java). This Hoeffding Tree includes a subspace size k parameter, which defines the number of randomly selected features to be considered at each split.
See details in:
Heitor Murilo Gomes, Albert Bifet, Jesse Read,
Jean Paul Barddal, Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes,
Talel Abdessalem. Adaptive random forests for evolving data stream classification.
In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017.
Modifier and Type | Class and Description |
---|---|
static class |
ARFHoeffdingTree.LearningNodeNB |
static class |
ARFHoeffdingTree.LearningNodeNBAdaptive |
static class |
ARFHoeffdingTree.RandomLearningNode |
HoeffdingTree.ActiveLearningNode, HoeffdingTree.FoundNode, HoeffdingTree.InactiveLearningNode, HoeffdingTree.LearningNode, HoeffdingTree.Node, HoeffdingTree.SplitNode
Modifier and Type | Field and Description |
---|---|
IntOption |
subspaceSizeOption |
activeLeafByteSizeEstimate, activeLeafNodeCount, binarySplitsOption, byteSizeEstimateOverheadFraction, decisionNodeCount, gracePeriodOption, growthAllowed, inactiveLeafByteSizeEstimate, inactiveLeafNodeCount, leafpredictionOption, maxByteSizeOption, maxTreeDepth, memoryEstimatePeriodOption, nbThresholdOption, nominalEstimatorOption, noPrePruneOption, numericEstimatorOption, removePoorAttsOption, splitConfidenceOption, splitCriterionOption, stopMemManagementOption, tieThresholdOption, treeRoot
classifierRandom, downSampleRatio, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
config
Constructor and Description |
---|
ARFHoeffdingTree() |
Modifier and Type | Method and Description |
---|---|
String |
getPurposeString()
Dictionary with option texts and objects
|
boolean |
isRandomizable()
Gets whether this learner needs a random seed.
|
protected HoeffdingTree.LearningNode |
newLearningNode(double[] initialClassObservations) |
activateLearningNode, attemptToSplit, calcByteSize, computeHoeffdingBound, deactivateAllLeaves, deactivateLearningNode, enforceTrackerLimit, estimateModelByteSizes, findLearningNodes, findLearningNodes, getModelDescription, getModelMeasurementsImpl, getVotesForInstance, measureByteSize, measureTreeDepth, newLearningNode, newNominalClassObserver, newNumericClassObserver, newSplitNode, newSplitNode, resetLearningImpl, trainOnInstanceImpl
contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getDownSampleRatio, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance, updateDownSampleRatio
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
copy, measureByteSize, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
getCLICreationString, getOptions, prepareForUse, prepareForUse
public IntOption subspaceSizeOption
public String getPurposeString()
AbstractOptionHandler
getPurposeString
in interface OptionHandler
getPurposeString
in class HoeffdingTree
protected HoeffdingTree.LearningNode newLearningNode(double[] initialClassObservations)
newLearningNode
in class HoeffdingTree
public boolean isRandomizable()
Learner
isRandomizable
in interface Learner<Example<Instance>>
isRandomizable
in class HoeffdingTree
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