See: Description
| Interface | Description |
|---|---|
| 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. |
| VectorThresholdLearner<OutputType> |
An interface class for decider learners that produce a threshold function
on a vector element based on maximizing some gain value.
|
| Class | Description |
|---|---|
| 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. |
| RandomForestFactory |
A factory class for creating Random Forest learners.
|
| 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 regression
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. |