Package gov.sandia.cognition.learning.algorithm.tree

Provides decision tree learning algorithms.

See:
          Description

Interface Summary
DeciderLearner<InputType,OutputType,CategoryType,DeciderType extends Categorizer<? super InputType,? extends CategoryType>> The DeciderLearner interface defines the functionality of a learner that can be used to learn a decision function inside a decision tree.
DecisionTreeNode<InputType,OutputType> The DecisionTreeNode interface defines the functionality of a node in a decision tree.
PriorWeightedNodeLearner<OutputType> The PriorWeightedNodeLearner interface specifies the ability to configure prior weights on the learning algorithm that searches for a decision function inside a decision tree.
VectorThresholdMaximumGainLearner<OutputType> An interface class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
 

Class Summary
AbstractDecisionTreeLearner<InputType,OutputType> The AbstractDecisionTreeLearner implements common functionality for learning algorithms that learn a decision tree.
AbstractDecisionTreeNode<InputType,OutputType,InteriorType> The AbstractDecisionTreeNode class implements common functionality for a decision tree node.
AbstractVectorThresholdMaximumGainLearner<OutputType> An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
CategorizationTree<InputType,OutputType> The CategorizationTree class extends the DecisionTree class to implement a decision tree that does categorization.
CategorizationTreeLearner<InputType,OutputType> The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.
CategorizationTreeNode<InputType,OutputType,InteriorType> The CategorizationTreeNode implements a DecisionTreeNode for a tree that does categorization.
DecisionTree<InputType,OutputType> The DecisionTree class implements a standard decision tree that is made up of DecisionTreeNode objects.
RandomSubVectorThresholdLearner<OutputType> Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.
RegressionTree<InputType> The RegressionTree class extends the DecisionTree class to implement a decision tree that does regression.
RegressionTreeLearner<InputType> The RegressionTreeLearner class implements a learning algorithm for a regression tree that makes use of a decider learner and a regresion learner.
RegressionTreeNode<InputType,InteriorType> The RegressionTreeNode implements a DecisionTreeNode for a tree that does regression.
VectorThresholdGiniImpurityLearner<OutputType> Learns vector thresholds based on the Gini impurity measure.
VectorThresholdHellingerDistanceLearner<OutputType> A categorization tree decision function learner on vector data that learns a vector value threshold function using the Hellinger distance.
VectorThresholdInformationGainLearner<OutputType> The VectorThresholdInformationGainLearner computes the best threshold over a dataset of vectors using information gain to determine the optimal index and threshold.
VectorThresholdVarianceLearner The VectorThresholdVarianceLearner computes the best threshold over a dataset of vectors using the reduction in variance to determine the optimal index and threshold.
 

Package gov.sandia.cognition.learning.algorithm.tree Description

Provides decision tree learning algorithms.

Since:
2.0
Author:
Justin Basilico