Package org.knime.base.node.mine.decisiontree2.learner

Class Summary

AtomicDouble	Implements an atomic double (i.e.
ClassValueDataRow	A data row represented as a double array.
DataRowWeighted	A data row represented as a double array.
DecisionTreeLearnerNodeDialog	Dialog for a decision tree learner node.
DecisionTreeLearnerNodeFactory	The Factory for the DecisionTreeLearnerNodeModel algorithm.
DecisionTreeLearnerNodeModel	Implements a decision tree induction algorithm based on C4.5 and SPRINT.
DecTreeLearnerGraphView
DecTreeNodeView	This node view is exactly the same as the one for the c4.5 encapsulating view.
InMemoryTable	Implements a table that holds DataRowWeighteds in memory.
InMemoryTableCreator	Creates an in memory representation of the given BufferedDataTable.
NominalValueHistogram	Represents a count histogram for nominal values.
ParallelProcessing	Holds information for the parallel processing and is also used for synchronization stuff.
Partitioner	Partitions a table according to a given split.
Pruner	Class implementing pruning schemes.
PruningResult	A pruning result is the possibly new node and a quality value (e.g.
Split	Calculates the best split for a given attribute list and the original class distribution.
SplitContinuous	This class determines the best split for a numeric attribute.
SplitFinder	Finds the best split for a given InMemoryTable.
SplitNominal	Super class for all nominal split variants.
SplitNominalBinary	This class determines the best binary split for a nominal attribute.
SplitNominalNormal	This class determines the best split for a nominal attribute.
SplitQualityGainRatio	Implements the gain ratio split quality measure.
SplitQualityGini	Implements the gini index split quality measure.
SplitQualityMeasure	The abstract class for split quality measures like gini or gain ratio.
ValueMapper<S>	A mapper mapping the objects of the defined class S to integer indices and vice versa.