Uses of Interface
com.rapidminer.report.Readable

Packages that use Readable

com.rapidminer.example	The data core classes of RapidMiner.
com.rapidminer.gui.renderer	This package consists the base classes for the renderers / visualization components of RapidMiner components and results.
com.rapidminer.gui.viewer	This package contain viewer classes for some standard data types like ExampleSets, DataTables etc.
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.performance	Provides performance evaluating operators and performance criteria.
com.rapidminer.operator.performance.cost	This package contains cost-based performance evaluations.
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.
com.rapidminer.operator.validation.significance	Statistical significance like ANOVA or t-tests.
com.rapidminer.tools.math	Several tool classes for mathematical operations.

Uses of Readable in com.rapidminer.example

Classes in com.rapidminer.example that implement Readable

class

AttributeWeight Helper class containing the name of an attribute and the corresponding weight.

Uses of Readable in com.rapidminer.gui.renderer

Classes in com.rapidminer.gui.renderer that implement Readable

class

DefaultReadable A simple default readable just build from a given text.

Uses of Readable in com.rapidminer.gui.viewer

Classes in com.rapidminer.gui.viewer that implement Readable

class

AverageVectorViewer

Uses of Readable in com.rapidminer.operator

Subinterfaces of Readable in com.rapidminer.operator

interface	Model Model is the interface for all objects which change a data set.
interface	ViewModel The view model is typically used for preprocessing models.

Classes in com.rapidminer.operator that implement Readable

class	AbstractModel Abstract model is the superclass for all objects which change a data set.
class	GroupedModel This model is a container for all models which should be applied in a sequence.

Uses of Readable in com.rapidminer.operator.clustering

Classes in com.rapidminer.operator.clustering that implement Readable

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 Readable in com.rapidminer.operator.features.transformation

Classes in com.rapidminer.operator.features.transformation that implement Readable

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 Readable in com.rapidminer.operator.learner

Subinterfaces of Readable in com.rapidminer.operator.learner

interface

FormulaProvider This interface indicates that the model is able to produce a human and machine readable formula which can then be parsed by other programs and used for predictions.

Classes in com.rapidminer.operator.learner that implement Readable

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 Readable in com.rapidminer.operator.learner.bayes

Classes in com.rapidminer.operator.learner.bayes that implement Readable

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 Readable in com.rapidminer.operator.learner.functions

Classes in com.rapidminer.operator.learner.functions that implement Readable

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 Readable in com.rapidminer.operator.learner.functions.kernel

Classes in com.rapidminer.operator.learner.functions.kernel that implement Readable

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 Readable in com.rapidminer.operator.learner.functions.kernel.evosvm

Classes in com.rapidminer.operator.learner.functions.kernel.evosvm that implement Readable

class

EvoSVMModel The model for the evolutionary SVM.

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

Classes in com.rapidminer.operator.learner.functions.kernel.hyperhyper that implement Readable

class

HyperModel The model for the HyperHyper implementation.

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

Classes in com.rapidminer.operator.learner.functions.neuralnet that implement Readable

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 Readable in com.rapidminer.operator.learner.igss.hypothesis

Classes in com.rapidminer.operator.learner.igss.hypothesis that implement Readable

class

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

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

Classes in com.rapidminer.operator.learner.lazy that implement Readable

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 Readable in com.rapidminer.operator.learner.local

Classes in com.rapidminer.operator.learner.local that implement Readable

class

LocalPolynomialRegressionModel

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

Classes in com.rapidminer.operator.learner.meta that implement Readable

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 Readable in com.rapidminer.operator.learner.rules

Classes in com.rapidminer.operator.learner.rules that implement Readable

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

Methods in com.rapidminer.operator.learner.rules that return Readable

Readable

RuleModel.getReadable(int index)

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

Classes in com.rapidminer.operator.learner.subgroups that implement Readable

class

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

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

Classes in com.rapidminer.operator.learner.tree that implement Readable

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 Readable in com.rapidminer.operator.performance

Classes in com.rapidminer.operator.performance that implement Readable

class	AbsoluteError The absolute error: Sum(|label-predicted|)/#examples.
class	AreaUnderCurve This criterion calculates the area under the ROC curve.
static class	AreaUnderCurve.Neutral
static class	AreaUnderCurve.Optimistic
static class	AreaUnderCurve.Pessimistic
class	BinaryClassificationPerformance This class encapsulates the well known binary classification criteria precision and recall.
class	CorrelationCriterion Computes the empirical corelation coefficient 'r' between label and prediction.
class	CrossEntropy Calculates the cross-entropy for the predictions of a classifier.
class	EstimatedPerformance This class is used to store estimated performance values before or even without the performance test is actually done using a test set.
class	LenientRelativeError The average relative error in a lenient way of calculation: Sum(|label-predicted|/max(|label|, |predicted|))/#examples.
class	LogisticLoss The logistic loss of a classifier, defined as the average over all ln(1 + exp(-y * f(x)))
class	Margin The margin of a classifier, defined as the minimal confidence for the correct label.
class	MDLCriterion Measures the length of an example set (i.e. the number of attributes).
class	MeasuredPerformance Superclass for performance citeria that are actually measured (not estimated).
class	MinMaxCriterion This criterion should be used as wrapper around other performance criteria (see MinMaxWrapper).
class	MultiClassificationPerformance Measures the accuracy and classification error for both binary classification problems and multi class problems.
class	NormalizedAbsoluteError Normalized absolute error is the total absolute error normalized by the error simply predicting the average of the actual values.
class	PerformanceCriterion Each PerformanceCriterion contains a method to compute this criterion on a given set of examples, each which has to have a real and a predicted label.
class	PredictionAverage Returns the average value of the prediction.
class	RankCorrelation Computes either the Spearman (rho) or Kendall (tau-b) rank correlation between the actual label and predicted values of an example set.
class	RelativeError The average relative error: Sum(|label-predicted|/label)/#examples.
class	RootMeanSquaredError The root-mean-squared error.
class	RootRelativeSquaredError Relative squared error is the total squared error made relative to what the error would have been if the prediction had been the average of the absolute value.
class	SimpleClassificationError This class calculates the classification error without determining the complete contingency table.
class	SimpleCriterion Simple criteria are those which error can be counted for each example and can be averaged by the number of examples.
class	SoftMarginLoss The soft margin loss of a classifier, defined as the average over all 1 - y * f(x).
class	SquaredCorrelationCriterion Computes the square of the empirical corellation coefficient 'r' between label and prediction.
class	SquaredError The squared error.
class	StrictRelativeError The average relative error in a strict way of calculation: Sum(|label-predicted|/min(|label|, |predicted|))/#examples.
class	WeightedMultiClassPerformance Measures the weighted mean of all per class recalls or per class precisions based on the weights defined in the performance evaluator.

Uses of Readable in com.rapidminer.operator.performance.cost

Classes in com.rapidminer.operator.performance.cost that implement Readable

class	ClassificationCostCriterion This performance Criterion works with a given cost matrix.
class	RankingCriterion This performance Criterion works with given ranking costs.

Uses of Readable in com.rapidminer.operator.postprocessing

Classes in com.rapidminer.operator.postprocessing that implement Readable

class

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

Uses of Readable in com.rapidminer.operator.preprocessing

Classes in com.rapidminer.operator.preprocessing that implement Readable

class	NoiseModel
class	PreprocessingModel Returns a more appropriate result icon.

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

Classes in com.rapidminer.operator.preprocessing.discretization that implement Readable

class

DiscretizationModel The generic discretization model.

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

Classes in com.rapidminer.operator.preprocessing.filter that implement Readable

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 Readable in com.rapidminer.operator.preprocessing.normalization

Classes in com.rapidminer.operator.preprocessing.normalization that implement Readable

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.

Uses of Readable in com.rapidminer.operator.validation.significance

Classes in com.rapidminer.operator.validation.significance that implement Readable

static class

TTestSignificanceTestOperator.TTestSignificanceTestResult The result for a paired t-test.

Uses of Readable in com.rapidminer.tools.math

Classes in com.rapidminer.tools.math that implement Readable

class

Averagable Superclass for all objects which can be averaged.