Package com.rapidminer.operator.learner.meta

Meta learning schemes which uses other learning operators to increase the performance.

See:
Description

Interface Summary

DelegationModel
MetaModel	This interface provides methods for accessing the different models encapsulated in this model for graphical representation.

Class Summary

AbstractMetaLearner	A MetaLearner is an operator that encapsulates one or more learning steps to build its model.
AbstractStacking	This class uses n+1 inner learners and generates n different models by using the last n learners.
AdaBoost	This AdaBoost implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.
AdaBoostModel	A model for the RapidMiner AdaBoost implementation.
AdaBoostPerformanceMeasures	Helper class for the internal AdaBoost implementation.
AdditiveRegression	This operator uses regression learner as a base learner.
AdditiveRegressionModel	The model created by an AdditiveRegression meta learner.
Bagging	This Bagging implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.
BaggingModel	The model for the internal Bagging implementation.
BayBoostBaseModelInfo	Stores a base model together with its contingency matrix, which offerers a more convenient access in the context of ensemble classification.
BayBoostModel	A model for the Bayesian Boosting algorithm by Martin Scholz.
BayBoostStream	Assumptions: target label is always boolean goal is to fit a crisp ensemble classifier (use_distribution always off) base classifier weights are always adapted by a single row from first to last no internal bootstrapping
BayBoostStream.BatchFilterCondition	Class that filters an ExampleSet by the value of a special attribute.
BayesianBoosting	This operator trains an ensemble of classifiers for boolean target attributes.
Binary2MultiClassLearner	A metaclassifier for handling multi-class datasets with 2-class classifiers.
Binary2MultiClassModel	This operator uses an inner learning scheme which is able to perform predictions for binary or binominal classification problems and learns a set of these binary models in order to use this set for a given data set with more than two classes.
ClassificationByRegression	For a classified dataset (with possibly more than two classes) builds a classifier using a regression method which is specified by the inner operator.
ContingencyMatrix	This class computes the contingency matrix of classifiers, supports weighted example sets and contains some convenience methods to query for some evaluation metrics that can directly be computed from this matrix.
CostBasedThresholdLearner	This operator uses a set of class weights and also allows a weight for the fact that an example is not classified at all (marked as unknown).
HierarchicalLearner	Deprecated.
HierarchicalModel	Deprecated.
HierarchicalModel.Node
HierarchicalMultiClassLearner	This is a meta learner for classifying multiple classes using a hierarchical approach.
HierarchicalMultiClassModel	This model of the hierarchical learner.
HierarchicalMultiClassModel.Node
MetaCost	This operator uses a given cost matrix to compute label predictions according to classification costs.
MetaCostModel	This class is associated to the MetaCost operator and supports the evaluation procedures of the MetaCost method.
MultiModelByRegression	MultiModels are used for multi class learning tasks.
RelativeRegression	This meta regression learner transforms the label on-the-fly relative to the value of the specified attribute.
RelativeRegressionModel	The model for the relative regression meta learner.
SDEnsemble	A subgroup discovery model.
SDReweightMeasures	A set of weighted performance measures used for subgroup discovery.
SDRulesetInduction	Subgroup discovery learner.
SimpleVoteModel	A simple vote model.
Stacking	This class uses n+1 inner learners and generates n different models by using the last n learners.
StackingModel	This class is the model build by the Stacking operator.
ThresholdModel	This model is created by the CostBasedThresholdLearner.
TransformedRegression	This meta learner applies a transformation on the label before the inner regression learner is applied.
TransformedRegressionModel	Model for TransformedRegression.
Tree2RuleConverter	This meta learner uses an inner tree learner and creates a rule model from the learned decision tree.
Vote	This class uses n+1 inner learners and generates n different models by using the last n learners.
WeightedPerformanceMeasures	This private class cares about weighted performance measures as used by the BayesianBoosting algorithm and the similarly working ModelBasedSampling operator.

Package com.rapidminer.operator.learner.meta Description

Meta learning schemes which uses other learning operators to increase the performance.