Package com.rapidminer.operator.learner.tree

Provides decision tree learners.

See:
Description

Interface Summary

LeafCreator	This class can be used to transform an inner tree node into a leaf.
MinimalGainHandler	This interface indicates that the criterion is able to handle a minimal gain prepruning.
Pruner	The pruner for trees.
SplitCondition	A condition for a split in decision tree, rules etc.
SplitPreprocessing	Will be used before each split.
Terminator	Implementations of this interface are used in order to determine if a splitting procedure should be stopped.

Class Summary

AbstractSplitCondition	The abstract super class for all split conditions.
AbstractTreeLearner	This is the abstract super class for all decision tree learners.
Benefit	Encapsulates some information about the benefit of a split.
CHAIDLearner	The CHAID decision tree learner works like the DecisionTreeLearner with one exception: it used a chi squared based criterion instead of the information gain or gain ratio criteria.
DecisionStumpLearner	This operator learns decision stumps, i.e. a small decision tree with only one single split.
DecisionTreeLeafCreator	This class can be used to transform an inner tree node into a leaf.
DecisionTreeLearner	This operator learns decision trees from both nominal and numerical data.
Edge	The class edge holds the information about a split condition to a tree (child).
EmptyTermination	Splitting should be terminated if the example set is empty.
FrequencyCalculator	Calculates frequencies and weights.
GreaterSplitCondition	Returns true if the value of the desired attribute is greater then a given threshold.
ID3Learner	This operator learns decision trees without pruning using nominal attributes only.
ID3NumericalLearner	This operator learns decision trees without pruning using both nominal and numerical attributes.
LessEqualsSplitCondition	A split condition for numerical values (less equals).
MaxDepthTermination	Terminates if a maximal depth is reached.
MinSizeTermination	Terminates if the example set has less than minSize examples.
MultiCriterionDecisionStumps	A DecisionStump clone that allows to specify different utility functions.
MultiCriterionDecisionStumps.DecisionStumpModel
MultiwayDecisionTree	This operator is a meta learner for numerical tree builder.
NoAttributeLeftTermination	Terminates if the example set does not have any regular attributes.
NominalSplitCondition	A split condition for nominal values (equals).
NumericalSplitter	Calculates the best split point for numerical attributes according to a given criterion.
PessimisticPruner	This class provides a pruner based on some heuristic statistics.
RandomForestLearner	This operators learns a random forest.
RandomForestModel	This model simply extends the SimpleVoteModel to avoid naming problems.
RandomSubsetPreprocessing	Selects a random subset.
RandomTreeLearner	This operator learns decision trees from both nominal and numerical data.
RelevanceTreeLearner	Learns a pruned decision tree based on arbitrary feature relevance measurements defined by an inner operator (use for example InfoGainRatioWeighting for C4.5 and ChiSquaredWeighting for CHAID.
SingleLabelTermination	This criterion terminates if only one single label is left.
Tree	A tree is a node in a tree model containing several edges to other trees (children) combined with conditions at these edges.
TreeBuilder	Build a tree from an example set.
TreeModel	The tree model is the model created by all decision trees.

Package com.rapidminer.operator.learner.tree Description

Provides decision tree learners.