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.
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.
 



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University of Konstanz, Germany.
Chair for Bioinformatics and Information Mining, Prof. Dr. Michael R. Berthold.
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