Uses of Class
com.rapidminer.operator.AbstractModel

Packages that use AbstractModel

com.rapidminer.operator	Provides operators for machine learning and data pre-processing.
com.rapidminer.operator.clustering	The base classes for clustering.
com.rapidminer.operator.features.transformation	Provides operators for feature space transformations like PCA or ICA.
com.rapidminer.operator.learner	Provides learning operators.
com.rapidminer.operator.learner.bayes	This package contains classes and operators for Naive Bayes learning.
com.rapidminer.operator.learner.functions	This package contains learners based on the concept of function approximation.
com.rapidminer.operator.learner.functions.kernel	Learning schemes which make use of kernel functions to transform the feature space, e.g. support vector machines.
com.rapidminer.operator.learner.functions.kernel.evosvm	Implementations of SVMs which makes use of general purpose optimization methods, e.g. evolutionary strategies or particle swarm optimization.
com.rapidminer.operator.learner.functions.kernel.hyperhyper	This package contains classes for the HyperHyper learner.
com.rapidminer.operator.learner.functions.neuralnet	This package contains a neural net learner based on Joone.
com.rapidminer.operator.learner.igss.hypothesis	Provides the hypothesis classes for learning operator Iterating Generic Sequential Sampling.
com.rapidminer.operator.learner.lazy	Learning schemes which perform lazy learning.
com.rapidminer.operator.learner.local
com.rapidminer.operator.learner.meta	Meta learning schemes which uses other learning operators to increase the performance.
com.rapidminer.operator.learner.rules	Provides rule learners.
com.rapidminer.operator.learner.subgroups	Provides the major classes of a subgroup discovery algorithm.
com.rapidminer.operator.learner.tree	Provides decision tree learners.
com.rapidminer.operator.postprocessing	Operators for post processing, usually used for models.
com.rapidminer.operator.preprocessing	Operators for preprocessing purposes.
com.rapidminer.operator.preprocessing.discretization	Contains discretization operators which can be used to transform numerical into nominal attributes.
com.rapidminer.operator.preprocessing.filter	Containing filter operators changing the input example set, e.g. by removing certain attributes or changing the data.
com.rapidminer.operator.preprocessing.normalization	Preprocessing operators used for normalization.

Uses of AbstractModel in com.rapidminer.operator

Subclasses of AbstractModel in com.rapidminer.operator

class

GroupedModel This model is a container for all models which should be applied in a sequence.

Uses of AbstractModel in com.rapidminer.operator.clustering

Subclasses of AbstractModel in com.rapidminer.operator.clustering

class	CentroidClusterModel This is the superclass for all centroid based cluster models and supports assigning unseen examples to the nearest centroid.
class	ClusterModel This class is the standard flat cluster model, using the example ids to remember which examples were assigned to which cluster.
class	FlatFuzzyClusterModel This class represents a stadard implementation of a flat, fuzzy clustering.

Uses of AbstractModel in com.rapidminer.operator.features.transformation

Subclasses of AbstractModel in com.rapidminer.operator.features.transformation

class	AbstractEigenvectorModel Abstract class for all eigenvector based operators providing methods for the renderer.
class	DimensionalityReducerModel The model for the generic dimensionality reducer.
class	FastICAModel This is the transformation model of the FastICA.
class	GHAModel This is the transformation model of the GHA The number of components is initially specified by the GHA.
class	KernelPCAModel The model for the Kernel-PCA.
class	PCAModel This is the transformation model of the principal components analysis.
class	SOMDimensionalityReductionModel The model for the SOM dimensionality reduction.

Uses of AbstractModel in com.rapidminer.operator.learner

Subclasses of AbstractModel in com.rapidminer.operator.learner

class	PredictionModel PredictionModel is the superclass for all objects generated by learners, i.e.
class	SimpleBinaryPredictionModel A model that can be applied to an example set by applying it to each example separately.
class	SimplePredictionModel A model that can be applied to an example set by applying it to each example separately.
class	UpdateablePredictionModel This is an abstract class for all updateable prediction models.

Uses of AbstractModel in com.rapidminer.operator.learner.bayes

Subclasses of AbstractModel in com.rapidminer.operator.learner.bayes

class	DiscriminantModel This is the model for discriminant analysis based learning schemes.
class	DistributionModel DistributionModel is a model for learners which estimate distributions of attribute values from example sets like NaiveBayes.
class	KernelDistributionModel KernelDistributionModel is a model for learners which estimate distributions of attribute values from example sets like NaiveBayes.
class	SimpleDistributionModel DistributionModel is a model for learners which estimate distributions of attribute values from example sets like NaiveBayes.

Uses of AbstractModel in com.rapidminer.operator.learner.functions

Subclasses of AbstractModel in com.rapidminer.operator.learner.functions

class	FastMarginModel This is the model of the fast margin learner which learns a linear SVM in linear time.
class	HyperplaneModel This model is a separating hyperplane for two classes.
class	LinearRegressionModel The model for linear regression.
class	LogisticRegressionModel The model determined by the LogisticRegression operator.
class	PolynomialRegressionModel The model for the polynomial regression.
class	SeeminglyUnrelatedRegressionModel This is the model of a SUR regression.
class	VectorRegressionModel The model for vector linear regression.

Uses of AbstractModel in com.rapidminer.operator.learner.functions.kernel

Subclasses of AbstractModel in com.rapidminer.operator.learner.functions.kernel

class	AbstractMySVMModel The abstract superclass for the SVM models by Stefan Rueping.
class	GPModel A model learned by the GPLearner.
class	JMySVMModel The implementation for the mySVM model (Java version) by Stefan Rueping.
class	KernelLogisticRegressionModel The model determined by the KernelLogisticRegression operator.
class	KernelModel This is the abstract model class for all kernel models.
class	LibSVMModel A model generated by the libsvm by Chih-Chung Chang and Chih-Jen Lin.
class	LinearMySVMModel The abstract superclass for the SVM models by Stefan Rueping.
class	MyKLRModel The model for the MyKLR learner by Stefan Rueping.
class	RVMModel A model generated by the RVMLearner.

Uses of AbstractModel in com.rapidminer.operator.learner.functions.kernel.evosvm

Subclasses of AbstractModel in com.rapidminer.operator.learner.functions.kernel.evosvm

class

EvoSVMModel The model for the evolutionary SVM.

Uses of AbstractModel in com.rapidminer.operator.learner.functions.kernel.hyperhyper

Subclasses of AbstractModel in com.rapidminer.operator.learner.functions.kernel.hyperhyper

class

HyperModel The model for the HyperHyper implementation.

Uses of AbstractModel in com.rapidminer.operator.learner.functions.neuralnet

Subclasses of AbstractModel in com.rapidminer.operator.learner.functions.neuralnet

class	ImprovedNeuralNetModel The model of the improved neural net.
class	NeuralNetModel This is the model for the neural net learner.
class	SimpleNeuralNetModel This is the model for the simple neural net learner.

Uses of AbstractModel in com.rapidminer.operator.learner.igss.hypothesis

Subclasses of AbstractModel in com.rapidminer.operator.learner.igss.hypothesis

class

GSSModel Wrapper class for rules found by the Iterating GSS algorithm.

Uses of AbstractModel in com.rapidminer.operator.learner.lazy

Subclasses of AbstractModel in com.rapidminer.operator.learner.lazy

class	AttributeBasedVotingModel Average model simply calculates the average of the attributes as prediction.
class	AttributeDefaultModel This variant of the DefaultModel sets the prediction according to another attribute given during learn time.
class	DefaultModel The default model sets the prediction of all examples to the mode value in case of nominal labels and to the average value in case of numerical labels.
class	KNNClassificationModel An implementation of a knn model.
class	KNNRegressionModel An implementation of a knn model used for regression

Uses of AbstractModel in com.rapidminer.operator.learner.local

Subclasses of AbstractModel in com.rapidminer.operator.learner.local

class

LocalPolynomialRegressionModel

Uses of AbstractModel in com.rapidminer.operator.learner.meta

Subclasses of AbstractModel in com.rapidminer.operator.learner.meta

class	AdaBoostModel A model for the RapidMiner AdaBoost implementation.
class	AdditiveRegressionModel The model created by an AdditiveRegression meta learner.
class	BaggingModel The model for the internal Bagging implementation.
class	BayBoostModel A model for the Bayesian Boosting algorithm by Martin Scholz.
class	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.
class	HierarchicalModel Deprecated.
class	HierarchicalMultiClassModel This model of the hierarchical learner.
class	MetaCostModel This class is associated to the MetaCost operator and supports the evaluation procedures of the MetaCost method.
class	MultiModelByRegression MultiModels are used for multi class learning tasks.
class	RelativeRegressionModel The model for the relative regression meta learner.
class	SDEnsemble A subgroup discovery model.
class	SimpleVoteModel A simple vote model.
class	StackingModel This class is the model build by the Stacking operator.
class	ThresholdModel This model is created by the CostBasedThresholdLearner.
class	TransformedRegressionModel Model for TransformedRegression.

Uses of AbstractModel in com.rapidminer.operator.learner.rules

Subclasses of AbstractModel in com.rapidminer.operator.learner.rules

class	ConjunctiveRuleModel Each object of this class represents a conjunctive rule with boolean target and nominal attributes.
class	RuleModel The basic rule model.

Uses of AbstractModel in com.rapidminer.operator.learner.subgroups

Subclasses of AbstractModel in com.rapidminer.operator.learner.subgroups

class

RuleSet A model consisting of rules which are scored by utility values.

Uses of AbstractModel in com.rapidminer.operator.learner.tree

Subclasses of AbstractModel in com.rapidminer.operator.learner.tree

static class	MultiCriterionDecisionStumps.DecisionStumpModel
class	RandomForestModel This model simply extends the SimpleVoteModel to avoid naming problems.
class	TreeModel The tree model is the model created by all decision trees.

Uses of AbstractModel in com.rapidminer.operator.postprocessing

Subclasses of AbstractModel in com.rapidminer.operator.postprocessing

class

PlattScalingModel A model that contains a boolean classifier and a scaling operation that turns confidence scores into probability estimates.

Uses of AbstractModel in com.rapidminer.operator.preprocessing

Subclasses of AbstractModel in com.rapidminer.operator.preprocessing

class	NoiseModel
class	PreprocessingModel Returns a more appropriate result icon.

Uses of AbstractModel in com.rapidminer.operator.preprocessing.discretization

Subclasses of AbstractModel in com.rapidminer.operator.preprocessing.discretization

class

DiscretizationModel The generic discretization model.

Uses of AbstractModel in com.rapidminer.operator.preprocessing.filter

Subclasses of AbstractModel in com.rapidminer.operator.preprocessing.filter

class	Dictionary Replaces strings by interpreting the second example set as a dictionary.
class	NominalToBinominalModel This model maps the values of all nominal values to binary attributes.
class	ValueReplenishmentModel This class provides the preprocessing model for all value replacing operators.

Uses of AbstractModel in com.rapidminer.operator.preprocessing.normalization

Subclasses of AbstractModel in com.rapidminer.operator.preprocessing.normalization

class	AbstractNormalizationModel
class	IQRNormalizationModel This is the normalization model for the IQR normalization method.
class	MinMaxNormalizationModel A simple model which can be used to transform all regular attributes into a value range between the given min and max values.
class	ProportionNormalizationModel This model is able to transform the data in a way, every transformed attribute of an example contains the proportion of the total sum of this attribute over all examples.
class	ZTransformationModel This model performs a z-Transformation on the given example set.