Uses of Interface
com.rapidminer.tools.LoggingHandler

Packages that use LoggingHandler

com.rapidminer.example	The data core classes of RapidMiner.
com.rapidminer.example.set	The available views (example sets) on the example tables.
com.rapidminer.example.table	The available example table implementations (data sources).
com.rapidminer.gui	Provides the main GUI classes.
com.rapidminer.operator	Provides operators for machine learning and data pre-processing.
com.rapidminer.operator.clustering	The base classes for clustering.
com.rapidminer.operator.clustering.clusterer	The operators for clustering.
com.rapidminer.operator.clustering.clusterer.soft	The operators and helper classes for soft clustering.
com.rapidminer.operator.features	Provides feature handling operators.
com.rapidminer.operator.features.aggregation	Provides operators for automatic feature aggregation.
com.rapidminer.operator.features.construction	Provides operators for automatic feature construction.
com.rapidminer.operator.features.selection	Provides operators for automatic feature selection.
com.rapidminer.operator.features.transformation	Provides operators for feature space transformations like PCA or ICA.
com.rapidminer.operator.features.weighting	Operators to weight features or determine feature relevance.
com.rapidminer.operator.generator	Provides operators for data generation.
com.rapidminer.operator.io	Operators to read data from files or write them into files.
com.rapidminer.operator.learner	Provides learning operators.
com.rapidminer.operator.learner.associations	This package contains classes and operators for association rule mining and frequent item set mining.
com.rapidminer.operator.learner.associations.fpgrowth	This package contains classes and operators for association rule mining and frequent item set mining based on FPGrowth.
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	Provides classes for learning operator Iterating Generic Sequential Sampling.
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.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.learner.weka	Operators which encapsulate the learning schemes provided by Weka.
com.rapidminer.operator.meta	Provides operators for experiment iteration, meta operators, and optimization.
com.rapidminer.operator.meta.branch	Provides operators for conditioned branching.
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.filter.attributes	This package contains the attribute filter.
com.rapidminer.operator.preprocessing.join	This package contains the operators for joining and merging example sets.
com.rapidminer.operator.preprocessing.normalization	Preprocessing operators used for normalization.
com.rapidminer.operator.preprocessing.outlier	Operators for outlier detection.
com.rapidminer.operator.preprocessing.sampling	Preprocessing operators used for sampling.
com.rapidminer.operator.preprocessing.series	Containing preprocessing operators for (time) series handling.
com.rapidminer.operator.preprocessing.series.filter	Containing preprocessing operators for (time) series filtering.
com.rapidminer.operator.preprocessing.transformation	This package contains some simple operators for basic transformations like grouping, aggregation and pivotization.
com.rapidminer.operator.preprocessing.weighting	This package methods for the weighting of examples.
com.rapidminer.operator.similarity	Basic framework for similarities.
com.rapidminer.operator.text	This package contains operators and objects to work on texts.
com.rapidminer.operator.validation	Operators for estimation of the performance which can be achieved by learning schemes (and other predictive operators).
com.rapidminer.operator.validation.clustering	Evaluation methods for clustering.
com.rapidminer.operator.validation.clustering.exampledistribution	Evaluation methods for clustering based on the distribution of examples.
com.rapidminer.operator.validation.significance	Statistical significance like ANOVA or t-tests.
com.rapidminer.operator.visualization	The operators in this package are used for visualization purposes.
com.rapidminer.operator.visualization.dependencies	The operators in this package are used for the calculation and the visualization of dependency matrices like those for correlations etc.
com.rapidminer.tools	Provides tools for RapidMiner like parsers for the input files.
com.rapidminer.tools.att	Provides tools for parsing the attribute description file.
com.rapidminer.tools.jdbc	Provides tools for database access via JDBC connections.
com.rapidminer.tools.math	Several tool classes for mathematical operations.
com.rapidminer.tools.math.optimization.ec.es	Evolutionary Strategies Optimization for real valued optimization problems.

Uses of LoggingHandler in com.rapidminer.example

Classes in com.rapidminer.example that implement LoggingHandler

class	AttributeWeight Helper class containing the name of an attribute and the corresponding weight.
class	AttributeWeights AttributeWeights holds the information about the weights of attributes of an example set.

Methods in com.rapidminer.example with parameters of type LoggingHandler

void	AttributeParser.generateAll(LoggingHandler logging, ExampleSet exampleSet, java.io.InputStream in) Parses all lines.
Attribute	AttributeParser.generateAttribute(LoggingHandler logging, java.lang.String constructionDescription, ExampleTable table)

Uses of LoggingHandler in com.rapidminer.example.set

Classes in com.rapidminer.example.set that implement LoggingHandler

class	AbstractExampleSet Implements wrapper methods of abstract example set.
class	AttributeSelectionExampleSet An implementation of ExampleSet that is only a fixed view on a selection of attributes of the parent example set.
class	AttributeWeightedExampleSet An implementation of ExampleSet that allows the weighting of the attributes.
class	ConditionedExampleSet Hides Examples that do not fulfill a given Condition.
class	HeaderExampleSet This example set is a clone of the attributes without reference to any data.
class	MappedExampleSet This example set uses a mapping of indices to access the examples provided by the parent example set.
class	ModelViewExampleSet This is a generic example set (view on the view stack of the data) which can be used to apply any preprocessing model and create a view from it.
class	NonSpecialAttributesExampleSet This example set treats all special attributes as regular attributes.
class	RemappedExampleSet This example set uses the mapping given by another example set and "remaps" on the fly the nominal values according to the given set.
class	ReplaceMissingExampleSet An implementation of ExampleSet that allows the replacement of missing values on the fly.
class	SimilarityExampleSet This similarity based example set is used for the operator ExampleSet2SimilarityExampleSet.
class	SimpleExampleSet A simple implementation of ExampleSet containing a list of attributes and a special attribute map.
class	SingleExampleExampleSet This view can be used to wrap a single example.
class	SortedExampleSet This example set uses a mapping of indices to access the examples provided by the parent example set.
class	SplittedExampleSet An example set that can be split into subsets by using a Partition.

Uses of LoggingHandler in com.rapidminer.example.table

Constructors in com.rapidminer.example.table with parameters of type LoggingHandler

IndexCachedDatabaseExampleTable(DatabaseHandler databaseHandler, java.lang.String tableName, int dataManagementType, boolean dropMappingTable, LoggingHandler logging)

Uses of LoggingHandler in com.rapidminer.gui

Constructors in com.rapidminer.gui with parameters of type LoggingHandler

DatabaseExampleVisualization(java.lang.String databaseURL, java.lang.String userName, java.lang.String password, int databaseSystem, java.lang.String tableName, java.lang.String columnName, LoggingHandler logging)

Uses of LoggingHandler in com.rapidminer.operator

Classes in com.rapidminer.operator that implement LoggingHandler

class	AbstractExampleSetProcessing Abstract superclass of all operators modifying an example set, i.e. accepting an ExampleSet as input and delivering an ExampleSet as output.
class	AbstractModel Abstract model is the superclass for all objects which change a data set.
class	CommandLineOperator This operator executes a system command.
class	DataMacroDefinitionOperator (Re-)Define macros for the current process.
class	FileEchoOperator This operator simply writed the specified text into the specified file.
class	GroupedModel This model is a container for all models which should be applied in a sequence.
class	IOConsumeOperator Most RapidMiner operators should define their desired input and delivered output in a senseful way.
class	IOMultiplyOperator In some cases you might want to apply different parts of the process on the same input object.
class	IORetrievalOperator This operator can be used to retrieve the IOObject which was previously stored under the specified name.
class	IOSelectOperator This operator allows to choose special IOObjects from the given input.
class	IOStorageOperator This operator can be used to store the given IOObject into the process under the specified name (the IOObject will be "hidden" and can not be directly accessed by following operators.
class	MacroConstructionOperator This operator constructs new macros from expressions which might also use already existing macros.
class	MacroDefinitionOperator (Re-)Define macros for the current process.
class	MemoryCleanUp Cleans up unused memory resources.
class	ModelApplier This operator applies a Model to an ExampleSet.
class	ModelGrouper This operator groups all input models together into a grouped (combined) model.
class	ModelUngrouper This operator ungroups a previously grouped model (ModelGrouper) and delivers the grouped input models.
class	ModelUpdater This operator updates a Model with an ExampleSet.
class	Operator An operator accepts an array of input objects and generates an array of output objects that can be processed by other operators.
class	OperatorChain A chain of operators that is subsequently applied.
class	ProcessRootOperator Each process must contain exactly one operator of this class and it must be the root operator of the process.
class	ResultObjectAdapter An adapter class for the interface ResultObject.
class	ScriptingOperator This operator can be used to execute arbitrary Groovy scripts.
class	SimpleOperatorChain A simple operator chain which can have an arbitrary number of inner operators.
class	SimpleResultObject A SimpleResulObject is only a helper class for very simple results with only a name and descriptive text.
class	SingleMacroDefinitionOperator (Re-)Define macros for the current process.
class	SQLExecution This operator performs an arbitrary SQL statement on an SQL database.

Methods in com.rapidminer.operator that return LoggingHandler

LoggingHandler	IOObject.getLog() Gets the logging associated with the operator currently working on this IOObject or the global log service if no operator was set.
LoggingHandler	AbstractIOObject.getLog() Gets the logging associated with the operator currently working on this IOObject or the global log service if no operator was set.

Methods in com.rapidminer.operator with parameters of type LoggingHandler

void	IOObject.setLoggingHandler(LoggingHandler loggingHandler) Sets the current working operator, i.e. the operator which is currently working on this IOObject.
void	AbstractIOObject.setLoggingHandler(LoggingHandler loggingHandler) Sets the current working operator, i.e. the operator which is currently working on this IOObject.

Uses of LoggingHandler in com.rapidminer.operator.clustering

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

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	ClusterModel2ExampleSet This Operator clusters an exampleset given a cluster model.
class	ClusterToPrediction This operator estimates a mapping between a given clustering and a prediction.
class	DendogramHierarchicalClusterModel This class only indicates that this model is providing information for plotting a dendogram.
class	FlatFuzzyClusterModel This class represents a stadard implementation of a flat, fuzzy clustering.
class	FlattenClusterModel Creates a flat cluster model from a hierarchical one by expanding nodes in the order of their distance until the desired number of clusters is reached.
class	HierarchicalClusterModel This class provides the data of a generic hierarchical cluster model.
class	WekaClusterModel A Weka clusterer which can be used to cluster examples.

Uses of LoggingHandler in com.rapidminer.operator.clustering.clusterer

Classes in com.rapidminer.operator.clustering.clusterer that implement LoggingHandler

class	AbstractClusterer Abstract superclass of clusterers which defines the I/O behavior.
class	AgglomerativeClustering This operator implements agglomerative clustering, providing the three different strategies SingleLink, CompleteLink and AverageLink.
class	DBScan This operator provides the DBScan cluster algorithm.
class	ExampleSet2ClusterModel This operator creates a flat cluster model using a nominal attribute and dividing the exampleset by this attribute over the clusters.
class	GenericWekaClustererAdaptor This operator performs the Weka clustering scheme with the same name.
class	KernelKMeans This operator is an implementation of kernel k means.
class	KMeans This operator represents an implementation of k-means.
class	KMedoids This operator represents an implementation of k-medoids.
class	RandomClustering Returns a random clustering.
class	SVClustering An implementation of Support Vector Clustering based on [BenHur/etal/2001a].
class	TopDownClustering A top-down generic clustering that can be used with any (flat) clustering as inner operator.

Uses of LoggingHandler in com.rapidminer.operator.clustering.clusterer.soft

Classes in com.rapidminer.operator.clustering.clusterer.soft that implement LoggingHandler

class

EMClusterer This operator represents an implementation of the EM-algorithm.

Uses of LoggingHandler in com.rapidminer.operator.features

Classes in com.rapidminer.operator.features that implement LoggingHandler

class	AbstractFeatureProcessing Superclass of all operators changing the features (attributes) of an ExampleSet.
class	AttributeWeightsApplier This operator deselects attributes with a weight value of 0.0.
class	FeatureOperator This class is the superclass of all feature selection and generation operators.

Uses of LoggingHandler in com.rapidminer.operator.features.aggregation

Classes in com.rapidminer.operator.features.aggregation that implement LoggingHandler

class

EvolutionaryFeatureAggregation Performs an evolutionary feature aggregation.

Uses of LoggingHandler in com.rapidminer.operator.features.construction

Classes in com.rapidminer.operator.features.construction that implement LoggingHandler

class	AbstractFeatureConstruction Abstract superclass of all feature processing operators who generate new features.
class	AbstractGeneratingGeneticAlgorithm In contrast to its superclass GeneticAlgorithm, the GeneratingGeneticAlgorithm generates new attributes and thus can change the length of an individual.
class	AGA Basically the same operator as the GeneratingGeneticAlgorithm operator.
class	AttributeAggregationOperator Allows to generate a new attribute which consists of a function of several other attributes.
class	AttributeConstruction This operator constructs new attributes from the attributes of the input example set.
class	CompleteFeatureGenerationOperator This operator applies a set of functions on all features of the input example set.
class	ConditionedFeatureGeneration Generates a new attribute and sets the attributes values according to the fulfilling of the specified conditions.
class	DirectedGGA DirectedGGA is an acronym for a Generating Genetic Algorithm which uses probability directed search heuristics to select attributes for generation or removing.
class	ExampleSetBasedFeatureOperator This class is the superclass of all feature selection and generation operators.
class	FeatureGenerationOperator This operator generates new user specified features.
class	FourierGGA FourierGGA has all functions of YAGGA2.
class	GaussFeatureConstructionOperator Creates a gaussian function based on a given attribute and a specified mean and standard deviation sigma.
class	GeneratingGeneticAlgorithm In contrast to the class GeneticAlgorithm, the GeneratingGeneticAlgorithm generates new attributes and thus can change the length of an individual.
class	LinearCombinationOperator This operator applies a linear combination for each vector of the input ExampleSet, i.e.
class	ProductGenerationOperator This operator creates all products of the specified attributes.
class	YAGGA YAGGA is an acronym for Yet Another Generating Genetic Algorithm.
class	YAGGA2 YAGGA is an acronym for Yet Another Generating Genetic Algorithm.

Uses of LoggingHandler in com.rapidminer.operator.features.selection

Classes in com.rapidminer.operator.features.selection that implement LoggingHandler

class	AbstractFeatureSelection Abstract superclass of all feature processing operators who remove features from the example set.
class	AbstractGeneticAlgorithm Genetic algorithms are general purpose optimization / search algorithms that are suitable in case of no or little problem knowledge.
class	AttributeWeightSelection This operator selects all attributes which have a weight fulfilling a given condition.
class	BruteForceSelection This feature selection operator selects the best attribute set by trying all possible combinations of attribute selections.
class	FeatureSelectionOperator This operator realizes the two deterministic greedy feature selection algorithms forward selection and backward elimination.
class	ForwardSelectionOperator
class	GeneticAlgorithm A genetic algorithm for feature selection (mutation=switch features on and off, crossover=interchange used features).
class	RandomSelection This operator selects a randomly chosen number of features randomly from the input example set.
class	RemoveCorrelatedFeatures Removes (un-) correlated features due to the selected filter relation.
class	RemoveUselessFeatures Removes useless attribute from the example set.
class	WeightGuidedSelectionOperator This operator uses input attribute weights to determine the order of features added to the feature set starting with the feature set containing only the feature with highest weight.

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

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

class	AbstractFeatureTransformation Abstract super class of all operators transforming the feature space.
class	DimensionalityReducer Abstract class representing some common functionality of dimensionality reduction methods.
class	DimensionalityReducerModel The model for the generic dimensionality reducer.
class	FastICA This operator performs the independent componente analysis (ICA).
class	FastICAModel This is the transformation model of the FastICA.
class	FourierTransform Creates a new example set consisting of the result of a fourier transformation for each attribute of the input example set.
class	GHA Generalized Hebbian Algorithm (GHA) is an iterative method to compute principal components.
class	GHAModel This is the transformation model of the GHA The number of components is initially specified by the GHA.
class	JamaDimensionalityReduction This class represents an abstract framework for performing dimensionality reduction using the JAMA package.
class	KernelPCA This operator performs a kernel-based principal components analysis (PCA).
class	KernelPCAModel The model for the Kernel-PCA.
class	PCA This operator performs a principal components analysis (PCA) using the covariance matrix.
class	PCAModel This is the transformation model of the principal components analysis.
class	PrincipalComponentsTransformation Builds the principal components of the given data.
class	SOMDimensionalityReduction This operator performs a dimensionality reduction based on a SOM (Self Organizing Map, aka Kohonen net).
class	SOMDimensionalityReductionModel The model for the SOM dimensionality reduction.
class	SVDReduction A dimensionality reduction method based on Singular Value Decomposition.

Uses of LoggingHandler in com.rapidminer.operator.features.weighting

Classes in com.rapidminer.operator.features.weighting that implement LoggingHandler

class	AbstractEntropyWeighting This operator calculates the relevance of a feature by computing the an entropy value of the class distribution, if the given example set would have been splitted according to the feature.
class	AbstractWeighting This is an abstract superclass for RapidMiner weighting operators.
class	AttributeWeights2ExampleSet This operator creates a new example set from the given attribute weights.
class	BackwardWeighting Uses the backward selection idea for the weighting of features.
class	ChiSquaredWeighting This operator calculates the relevance of a feature by computing for each attribute of the input example set the value of the chi-squared statistic with respect to the class attribute.
class	ComponentWeights For models creating components like PCA, GHA and FastICA you can create the AttributeWeights from a component.
class	CorpusBasedFeatureWeighting This operator uses a corpus of examples to characterize a single class by setting feature weights.
class	CorrelationWeighting This class provides a weighting scheme based upon correlation.
class	EvolutionaryWeighting This operator performs the weighting of features with an evolutionary strategies approach.
class	ExampleSet2AttributeWeights This operator creates a new attribute weights IOObject from a given example set.
class	FeatureWeighting This operator performs the weighting under the naive assumption that the features are independent from each other.
class	ForwardWeighting This operator performs the weighting under the naive assumption that the features are independent from each other.
class	GenericWekaAttributeWeighting Performs the AttributeEvaluator of Weka with the same name to determine a sort of attribute relevance.
class	GiniWeighting This operator calculates the relevance of a feature by computing the Gini index of the class distribution, if the given example set would have been splitted according to the feature.
class	InfoGainRatioWeighting This operator calculates the relevance of a feature by computing the information gain ratio for the class distribution (if exampleSet would have been splitted according to each of the given features).
class	InfoGainWeighting This operator calculates the relevance of a feature by computing the information gain in class distribution, if exampleSet would be splitted after the feature.
class	InteractiveAttributeWeighting This operator shows a window with the currently used attribute weights and allows users to change the weight interactively.
class	NameBasedWeighting This operator is able to create feature weights based on regular expressions defined for the feature names.
class	OneRErrorWeighting This operator calculates the relevance of a feature by computing the error rate of a OneR Model on the exampleSet without this feature.
class	PCAWeighting Uses the factors of one of the principal components (default is the first) as feature weights.
class	ProcessLog2AttributeWeights This operator creates attribute weights from an attribute column in the statistics created by the ProcessLog operator.
class	PSOWeighting This operator performs the weighting of features with a particle swarm approach.
class	ReliefWeighting Relief measures the relevance of features by sampling examples and comparing the value of the current feature for the nearest example of the same and of a different class.
class	StandardDeviationWeighting Creates weights from the standard deviations of all attributes.
class	SVMWeighting Uses the coefficients of the normal vector of a linear SVM as feature weights.
class	SymmetricalUncertaintyOperator This operator calculates the relevance of an attribute by measuring the symmetrical uncertainty with respect to the class.

Uses of LoggingHandler in com.rapidminer.operator.generator

Classes in com.rapidminer.operator.generator that implement LoggingHandler

class	ChurnReductionExampleSetGenerator Generates a random example set for testing purposes.
class	DirectMailingExampleSetGenerator Generates a random example set for testing purposes.
class	ExampleSetGenerator Generates a random example set for testing purposes.
class	MassiveDataGenerator Generates huge amounts of data in either sparse or dense format.
class	MultipleLabelGenerator Generates a random example set for testing purposes with more than one label.
class	NominalExampleSetGenerator Generates a random example set for testing purposes.
class	SalesExampleSetGenerator Generates a random example set for testing purposes.
class	TeamProfitExampleSetGenerator Generates a random example set for testing purposes.
class	TransfersExampleSetGenerator Generates a random example set for testing purposes.
class	UpSellingExampleSetGenerator Generates a random example set for testing purposes.

Uses of LoggingHandler in com.rapidminer.operator.io

Classes in com.rapidminer.operator.io that implement LoggingHandler

class	AbstractExampleSetWriter Abstract super type of example set writing operators.
class	AbstractExampleSource Super class of all operators requiring no input and creating an ExampleSet.
class	AbstractModelLoader Super class of all operators requiring no input and creating a Model.
class	AbstractReader<T extends IOObject> Superclass of all operators that have no input and generate a single output.
class	AbstractWriter<T extends IOObject> Superclass of all operators that take a single object as input, save it to disk and return the same object as output.
class	AccessExampleSource This operator can be used to simplify the reading of MS Access databases.
class	ArffExampleSetWriter Writes values of all examples into an ARFF file which can be used by the machine learning library Weka.
class	ArffExampleSource This operator can read ARFF files known from the machine learning library Weka.
class	AttributeConstructionsLoader Loads an attribute set from a file and constructs the desired features.
class	AttributeConstructionsWriter Writes all attributes of an example set to a file.
class	AttributeWeightsLoader Reads the weights for all attributes of an example set from a file and creates a new AttributeWeights IOObject.
class	AttributeWeightsWriter Writes the weights of all attributes of an example set to a file.
class	BibtexExampleSource This operator can read BibTeX files.
class	BytewiseExampleSource Superclass for file data source operators which read the file byte per byte into a byte array and extract the actual data from that array.
class	C45ExampleSource Loads data given in C4.5 format (names and data file).
class	CachedDatabaseExampleSource This operator reads an ExampleSet from an SQL database.
class	ClusterModelReader Reads a single cluster model from a file.
class	ClusterModelWriter Write a single cluster model to a file.
class	CSVExampleSetWriter This operator can be used to write data into CSV files (Comma Separated Values).
class	CSVExampleSource This operator can read csv files.
class	DasyLabDataReader This operator allows to import data from DasyLab files (.DDF) into RapidMiner.
class	DatabaseExampleSetWriter This operator writes an ExampleSet into an SQL database.
class	DatabaseExampleSource This operator reads an ExampleSet from an SQL database.
class	DBaseExampleSource This operator can read dbase files.
class	ExampleSetWriter Writes values of all examples in an ExampleSet to a file.
class	ExampleSource This operator reads an example set from (a) file(s).
class	ExcelExampleSetWriter This operator can be used to write data into Microsoft Excel spreadsheets.
class	ExcelExampleSource This operator can be used to load data from Microsoft Excel spreadsheets.
class	GNUPlotOperator Writes the data generated by a ProcessLogOperator to a file in gnuplot format.
class	IOContainerReader Reads all elements of an IOContainer from a file.
class	IOContainerWriter Writes all elements of the current IOContainer, i.e. all objects passed to this operator, to a file.
class	IOObjectReader Generic reader for all types of IOObjects.
class	IOObjectWriter Generic writer for all types of IOObjects.
class	KDBExampleSource This class can read arff, comma separated values (csv), dbase and bibtex files.
class	ModelLoader Reads a Model from a file that was generated by an operator like Learner in a previous process.
class	ModelWriter Writes the input model in the file specified by the corresponding parameter.
class	ParameterSetLoader Reads a set of parameters from a file that was written by a ParameterOptimizationOperator.
class	ParameterSetWriter Writes a parameter set into a file.
class	PerformanceLoader Reads a performance vector from a given file.
class	PerformanceWriter Writes the input performance vector in a given file.
class	ResultSetExampleSource Abstract superclass for operators that provide access to an ExampleSet via a ResultSet.
class	ResultWriter This operator can be used at each point in an operator chain.
class	SimpleExampleSource This operator reads an example set from (a) file(s).
class	SparseFormatExampleSource Reads an example file in sparse format, i.e. lines have the form label index:value index:value index:value...
class	SPSSExampleSource This operator can read spss files.
class	StataExampleSource This operator can read stata files.
class	ThresholdLoader Reads a threshold from a file.
class	ThresholdWriter Writes the given threshold into a file.
class	URLExampleSource This operator reads an example set from an URL.
class	WekaModelLoader This operator reads in model files which were saved from the Weka toolkit.
class	XrffExampleSetWriter Writes values of all examples into an XRFF file which can be used by the machine learning library Weka.
class	XrffExampleSource This operator can read XRFF files known from Weka.

Uses of LoggingHandler in com.rapidminer.operator.learner

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

class	AbstractLearner A Learner is an operator that encapsulates the learning step of a machine learning method.
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.

Uses of LoggingHandler in com.rapidminer.operator.learner.associations

Classes in com.rapidminer.operator.learner.associations that implement LoggingHandler

class	AssociationRuleGenerator This operator generates association rules from frequent item sets.
class	AssociationRules A set of AssociationRules which can be constructed from frequent item sets.
class	FrequentItemSetAttributeCreator This operator takes all FrequentItemSet sets within IOObjects and creates attributes for every frequent item set.
class	FrequentItemSets Contains a collection of FrequentItemSets.
class	FrequentItemSetUnificator This operator compares a number of FrequentItemSet sets and removes every not unique FrequentItemSet.

Uses of LoggingHandler in com.rapidminer.operator.learner.associations.fpgrowth

Classes in com.rapidminer.operator.learner.associations.fpgrowth that implement LoggingHandler

class

FPGrowth This operator calculates all frequent items sets from a data set by building a FPTree data structure on the transaction data base.

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

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

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	KernelNaiveBayes Kernel Naive Bayes learner.
class	LinearDiscriminantAnalysis This operator performs a linear discriminant analysis (LDA).
class	NaiveBayes Naive Bayes learner.
class	QuadraticDiscriminantAnalysis This operator performs a quadratic discriminant analysis (QDA).
class	RegularizedDiscriminantAnalysis This is a regularized discriminant analysis (RDA) which is a generalization of the LDA or QDA.
class	SimpleDistributionModel DistributionModel is a model for learners which estimate distributions of attribute values from example sets like NaiveBayes.

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

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

class	FastLargeMargin Applies a fast margin learner based on the linear support vector learning scheme proposed by R.
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	LinearRegression This operator calculates a linear regression model.
class	LinearRegressionModel The model for linear regression.
class	LogisticRegression This operator determines a logistic regression model.
class	LogisticRegressionModel The model determined by the LogisticRegression operator.
class	Perceptron The perceptron is a type of artificial neural network invented in 1957 by Frank Rosenblatt.
class	PolynomialRegression This regression learning operator fits a polynomial of all attributes to the given data set.
class	PolynomialRegressionModel The model for the polynomial regression.
class	VectorLinearRegression This operator performs a vector linear regression.
class	VectorRegressionModel The model for vector linear regression.

Constructors in com.rapidminer.operator.learner.functions with parameters of type LoggingHandler

LogisticRegressionOptimization(ExampleSet exampleSet, boolean addIntercept, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary optimization.

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

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

class	AbstractMySVMLearner This is the abstract superclass for the support vector machine / KLR implementations of Stefan Rüping.
class	AbstractMySVMModel The abstract superclass for the SVM models by Stefan Rueping.
class	GPLearner Gaussian Process (GP) Learner.
class	GPModel A model learned by the GPLearner.
class	JMySVMLearner This learner uses the Java implementation of the support vector machine mySVM by Stefan Rüping.
class	JMySVMModel The implementation for the mySVM model (Java version) by Stefan Rueping.
class	KernelLogisticRegression This operator determines a logistic regression model.
class	KernelLogisticRegressionModel The model determined by the KernelLogisticRegression operator.
class	KernelModel This is the abstract model class for all kernel models.
class	LibSVMLearner Applies the libsvm learner by Chih-Chung Chang and Chih-Jen Lin.
class	LibSVMModel A model generated by the libsvm by Chih-Chung Chang and Chih-Jen Lin.
class	MyKLRLearner This is the Java implementation of myKLR by Stefan Rüping.
class	MyKLRModel The model for the MyKLR learner by Stefan Rueping.
class	RVMLearner Relevance Vector Machine (RVM) Learner.
class	RVMModel A model generated by the RVMLearner.

Constructors in com.rapidminer.operator.learner.functions.kernel with parameters of type LoggingHandler

KernelLogisticRegressionOptimization(ExampleSet exampleSet, Kernel kernel, double c, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

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

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

class	EvoSVM This is a SVM implementation using an evolutionary algorithm (ES) to solve the dual optimization problem of a SVM.
class	EvoSVMModel The model for the evolutionary SVM.
class	PSOSVM This is a SVM implementation using a particle swarm optimization (PSO) approach to solve the dual optimization problem of a SVM.

Constructors in com.rapidminer.operator.learner.functions.kernel.evosvm with parameters of type LoggingHandler

ClassificationEvoOptimization(ExampleSet exampleSet, Kernel kernel, double c, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, ExampleSet holdOutSet, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

RegressionEvoOptimization(ExampleSet exampleSet, Kernel kernel, double c, double epsilon, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

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

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

class	HyperHyper This is a minimal SVM implementation.
class	HyperModel The model for the HyperHyper implementation.

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

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

class	ImprovedNeuralNetLearner This operator learns a model by means of a feed-forward neural network trained by a backpropagation algorithm (multi-layer perceptron).
class	ImprovedNeuralNetModel The model of the improved neural net.
class	NeuralNetLearner This operator learns a model by means of a feed-forward neural network.
class	NeuralNetModel This is the model for the neural net learner.
class	SimpleNeuralNetLearner This operator learns a model by means of a feed-forward neural network.
class	SimpleNeuralNetModel This is the model for the simple neural net learner.

Uses of LoggingHandler in com.rapidminer.operator.learner.igss

Classes in com.rapidminer.operator.learner.igss that implement LoggingHandler

class	IGSSResult This class stores all results found by the IGSS algorithm.
class	IteratingGSS This operator implements the IteratingGSS algorithmus presented in the diploma thesis 'Effiziente Entdeckung unabhaengiger Subgruppen in grossen Datenbanken' at the Department of Computer Science, University of Dortmund.

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

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

class

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

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

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

class	AttributeBasedVotingLearner AttributeBasedVotingLearner is very lazy.
class	AttributeBasedVotingModel Average model simply calculates the average of the attributes as prediction.
class	DefaultLearner This learner creates a model, that will simply predict a default value for all examples, i.e. the average or median of the true labels (or the mode in case of classification) or a fixed specified value.
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	KNNLearner A k nearest neighbor implementation.
class	KNNRegressionModel An implementation of a knn model used for regression

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

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

class	AbstractMetaLearner A MetaLearner is an operator that encapsulates one or more learning steps to build its model.
class	AbstractStacking This class uses n+1 inner learners and generates n different models by using the last n learners.
class	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.
class	AdaBoostModel A model for the RapidMiner AdaBoost implementation.
class	AdditiveRegression This operator uses regression learner as a base learner.
class	AdditiveRegressionModel The model created by an AdditiveRegression meta learner.
class	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.
class	BaggingModel The model for the internal Bagging implementation.
class	BayBoostModel A model for the Bayesian Boosting algorithm by Martin Scholz.
class	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
class	BayesianBoosting This operator trains an ensemble of classifiers for boolean target attributes.
class	Binary2MultiClassLearner A metaclassifier for handling multi-class datasets with 2-class classifiers.
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	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.
class	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).
class	MetaCost This operator uses a given cost matrix to compute label predictions according to classification costs.
class	MetaCostModel This class is associated to the MetaCost operator and supports the evaluation procedures of the MetaCost method.
class	MultiModel MultiModels are used for multi class learning tasks.
class	MultiModelByRegression MultiModels are used for multi class learning tasks.
class	RelativeRegression This meta regression learner transforms the label on-the-fly relative to the value of the specified attribute.
class	RelativeRegressionModel The model for the relative regression meta learner.
class	SDEnsemble A subgroup discovery model.
class	SDRulesetInduction Subgroup discovery learner.
class	SimpleVoteModel A simple vote model.
class	Stacking This class uses n+1 inner learners and generates n different models by using the last n learners.
class	StackingModel This class is the model build by the Stacking operator.
class	ThresholdModel This model is created by the CostBasedThresholdLearner.
class	TransformedRegression This meta learner applies a transformation on the label before the inner regression learner is applied.
class	TransformedRegressionModel Model for TransformedRegression.
class	Tree2RuleConverter This meta learner uses an inner tree learner and creates a rule model from the learned decision tree.
class	Vote This class uses n+1 inner learners and generates n different models by using the last n learners.

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

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

class	BestRuleInduction This operator returns the best rule regarding WRAcc using exhaustive search.
class	ConjunctiveRuleModel Each object of this class represents a conjunctive rule with boolean target and nominal attributes.
class	RuleLearner This operator works similar to the propositional rule learner named Repeated Incremental Pruning to Produce Error Reduction (RIPPER, Cohen 1995).
class	RuleModel The basic rule model.
class	SimpleRuleLearner This operator builds an unpruned rule set of classification rules.
class	SingleRuleLearner This operator concentrates on one single attribute and determines the best splitting terms for minimizing the training error.

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

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

class	RuleSet A model consisting of rules which are scored by utility values.
class	SubgroupDiscovery This operator discovers subgroups (or induces a rule set, respectively) by generating hypotheses exhaustively.

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

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

class	AbstractTreeLearner This is the abstract super class for all decision tree learners.
class	CHAIDLearner The CHAID decision tree learner works like the DecisionTreeLearner with one exception: it used a chi squared based criterion instead of the information gain or gain ratio criteria.
class	DecisionStumpLearner This operator learns decision stumps, i.e. a small decision tree with only one single split.
class	DecisionTreeLearner This operator learns decision trees from both nominal and numerical data.
class	ID3Learner This operator learns decision trees without pruning using nominal attributes only.
class	ID3NumericalLearner This operator learns decision trees without pruning using both nominal and numerical attributes.
class	MultiCriterionDecisionStumps A DecisionStump clone that allows to specify different utility functions.
static class	MultiCriterionDecisionStumps.DecisionStumpModel
class	MultiwayDecisionTree This operator is a meta learner for numerical tree builder.
class	RandomForestLearner This operators learns a random forest.
class	RandomTreeLearner This operator learns decision trees from both nominal and numerical data.
class	RelevanceTreeLearner Learns a pruned decision tree based on arbitrary feature relevance measurements defined by an inner operator (use for example InfoGainRatioWeighting for C4.5 and ChiSquaredWeighting for CHAID.
class	TreeModel The tree model is the model created by all decision trees.

Uses of LoggingHandler in com.rapidminer.operator.learner.weka

Classes in com.rapidminer.operator.learner.weka that implement LoggingHandler

class	GenericWekaAssociationLearner Performs the Weka association rule learner with the same name.
class	GenericWekaEnsembleLearner Performs the ensemble learning scheme of Weka with the same name.
class	GenericWekaLearner Performs the Weka learning scheme with the same name.
class	GenericWekaMetaLearner Performs the meta learning scheme of Weka with the same name.
class	WekaAssociator In contrast to models generated by normal learners, the association rules cannot be applied to an example set.
class	WekaClassifier A Weka Classifier which can be used to classify Examples.

Uses of LoggingHandler in com.rapidminer.operator.meta

Classes in com.rapidminer.operator.meta that implement LoggingHandler

class	AbsoluteSplitChain An operator chain that split an ExampleSet into two disjunct parts and applies the first child operator on the first part and applies the second child on the second part and the result of the first child.
class	AbstractSplitChain An operator chain that split an ExampleSet into two disjunct parts and applies the first child operator on the first part and applies the second child on the second part and the result of the first child.
class	AverageBuilder Collects all average vectors (e.g.
class	BatchProcessing This operator groups the input examples into batches of the specified size and performs the inner operators on all batches subsequently.
class	ClusterIterator This operator splits up the input example set according to the clusters and applies its inner operators number_of_clusters time.
class	EvolutionaryParameterOptimizationOperator This operator finds the optimal values for a set of parameters using an evolutionary strategies approach which is often more appropriate than a grid search or a greedy search like the quadratic programming approach and leads to better results.
class	ExampleSetIterator For each example set the ExampleSetIterator finds in its input, the inner operators are applied as if it was an OperatorChain.
class	ExceptionHandling This operator performs the inner operators and delivers the result of the inner operators.
class	FeatureIterator This operator takes an input data set and applies its inner operators as often as the number of features of the input data is.
class	FeatureSubsetIteration This meta operator iterates through all possible feature subsets within the specified range and applies the inner operators on the feature subsets.
class	FileIterator This operator iterates over the files in the specified directory (and subdirectories if the corresponding parameter is set to true).
class	GridSearchParameterOptimizationOperator This operator finds the optimal values for a set of parameters using a grid search.
class	IteratingOperatorChain Performs its inner operators for the defined number of times.
class	IterativeWeightOptimization Performs an iterative feature selection guided by the AttributeWeights.
class	LearningCurveOperator This operator first divides the input example set into two parts, a training set and a test set according to the parameter "training_ratio".
class	MultipleLabelIterator Performs the inner operator for all label attributes, i.e. special attributes whose name starts with "label".
class	OperatorEnabler This operator can be used to enable and disable other operators.
class	OperatorSelector This operator can be used to employ a single inner operator or operator chain.
class	ParameterCloner Sets a list of parameters using existing parameter values.
class	ParameterIteratingOperatorChain Provides an operator chain which operates on given parameters depending on specified values for these parameters.
class	ParameterIteration In contrast to the GridSearchParameterOptimizationOperator operator this operators simply uses the defined parameters and perform the inner operators for all possible combinations.
class	ParameterOptimizationOperator This operator provides basic functions for all other parameter optimization operators.
class	ParameterSet A set of parameters generated by a ParameterOptimizationOperator.
class	ParameterSetter Sets a set of parameters.
class	PartialExampleSetLearner This operator works similar to the LearningCurveOperator.
class	ProcessEmbeddingOperator This operator can be used to embed a complete process definition into the current process definition.
class	QuadraticParameterOptimizationOperator This operator finds the optimal values for a set of parameters using a quadratic interaction model.
class	RandomOptimizationChain This operator iterates several times through the inner operators and in each cycle evaluates a performance measure.
class	RatioSplitChain An operator chain that split an ExampleSet into two disjunct parts and applies the first child operator on the first part and applies the second child on the second part and the result of the first child.
class	RepeatUntilOperatorChain Performs its inner operators until all given criteria are met or a timeout occurs.
class	UnivariateLabelSeriesPrediction This operator can be used for some basic series prediction operations.
class	ValueIteration In each iteration step, this meta operator applies its inner operators to the input example set.
class	ValueSubgroupIteration In each iteration step, this meta operator applies its inner operators to a subset of the input example set.
class	WeightOptimization Performs a feature selection guided by the AttributeWeights.
class	XVPrediction Operator chain that splits an ExampleSet into a training and test sets similar to XValidation, but returns the test set predictions instead of a performance vector.

Constructors in com.rapidminer.operator.meta with parameters of type LoggingHandler

ESParameterOptimization(EvolutionaryParameterOptimizationOperator operator, int individualSize, int initType, int maxIterations, int generationsWithoutImprovement, int popSize, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

Uses of LoggingHandler in com.rapidminer.operator.meta.branch

Classes in com.rapidminer.operator.meta.branch that implement LoggingHandler

class

ProcessBranch This operator provides a conditional execution of parts of processes.

Uses of LoggingHandler in com.rapidminer.operator.performance

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

class	AbsoluteError The absolute error: Sum(|label-predicted|)/#examples.
class	AbstractExampleSetEvaluator Abstract superclass of operators accepting an ExampleSet and producing a PerformanceVector.
class	AbstractPerformanceEvaluator This performance evaluator operator should be used for regression tasks, i.e. in cases where the label attribute has a numerical value type.
class	AreaUnderCurve This criterion calculates the area under the ROC curve.
class	AttributeCounter Returns a performance vector just counting the number of attributes currently used for the given example set.
class	BinaryClassificationPerformance This class encapsulates the well known binary classification criteria precision and recall.
class	BinominalClassificationPerformanceEvaluator This performance evaluator operator should be used for classification tasks, i.e. in cases where the label attribute has a binominal value type.
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	Data2Performance This operator can be used to derive a specific value of a given example set and provide it as a performance value which can be used for optimization purposes.
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	ForecastingPerformanceEvaluator This operator calculates performance criteria related to series forecasting / prediction.
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	MinMaxWrapper Wraps a MinMaxCriterion around each performance criterion of type MeasuredPerformance.
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	PerformanceEvaluator A performance evaluator is an operator that expects a test ExampleSet as input, whose elements have both true and predicted labels, and delivers as output a list of performance values according to a list of performance criteria that it calculates.
class	PerformanceVector Handles several performance criteria.
class	PolynominalClassificationPerformanceEvaluator This performance evaluator operator should be used for classification tasks, i.e. in cases where the label attribute has a (poly-)nominal value type.
class	PredictionAverage Returns the average value of the prediction.
class	PredictionTrendAccuracy Measures the number of times a regression prediction correctly determines the trend.
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	RegressionPerformanceEvaluator This performance evaluator operator should be used for regression tasks, i.e. in cases where the label attribute has a numerical value type.
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	SimplePerformanceEvaluator In contrast to the other performance evaluation methods, this performance evaluator operator can be used for all types of learning tasks.
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	SupportVectorCounter Returns a performance vector just counting the number of support vectors of a given support vector based model (kernel model).
class	UserBasedPerformanceEvaluator This performance evaluator operator should be used for regression tasks, i.e. in cases where the label attribute has a numerical value type.
class	WeightedMultiClassPerformance Measures the weighted mean of all per class recalls or per class precisions based on the weights defined in the performance evaluator.
class	WeightedPerformanceCreator Returns a performance vector containing the weighted fitness value of the input criteria.

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

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

class	ClassificationCostCriterion This performance Criterion works with a given cost matrix.
class	CostEvaluator This operator provides the ability to evaluate classification costs.

Uses of LoggingHandler in com.rapidminer.operator.postprocessing

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

class	PlattScaling A scaling operator, applying the original algorithm by Platt (1999) to turn confidence scores of boolean classifiers into probability estimates.
class	PlattScalingModel A model that contains a boolean classifier and a scaling operation that turns confidence scores into probability estimates.
class	SimpleUncertainPredictionsTransformation This operator sets all predictions which do not have a higher confidence than the specified one to "unknown" (missing value).
class	Threshold A threshold for soft2crisp classifying.
class	ThresholdApplier This operator applies the given threshold to an example set and maps a soft prediction to crisp values.
class	ThresholdCreator This operator creates a user defined threshold for crisp classifying based on prediction confidences.
class	ThresholdFinder This operator finds the best threshold for crisp classifying based on user defined costs.
class	WindowExamples2OriginalData This operator performs several transformations which could be performed by basic RapidMiner operators but lead to complex operator chains.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing

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

class	AbstractDataProcessing Abstract super class of the AbstractExampleSetProcessing hierarchy in the preprocessing package.
class	AttributeSubsetPreprocessing This operator can be used to select one attribute (or a subset) by defining a regular expression for the attribute name and applies its inner operators to the resulting subset.
class	Deobfuscator This operator takes an ExampleSet as input and maps all nominal values to randomly created strings.
class	ExampleSetTranspose This operator transposes an example set, i.e. the columns with become the new rows and the old rows will become the columns.
class	GroupByOperator This operator creates a SplittedExampleSet from an arbitrary example set.
class	GuessValueTypes This operator can be used to (re-)guess the value types of all attributes.
class	IdTagging This operator adds an ID attribute to the given example set.
class	MaterializeDataInMemory Creates a fresh and clean copy of the data in memory.
class	NoiseOperator This operator adds random attributes and white noise to the data.
class	Obfuscator This operator takes an ExampleSet as input and maps all nominal values to randomly created strings.
class	PreprocessingModel Returns a more appropriate result icon.
class	PreprocessingOperator Superclass for all preprocessing operators.
class	UseRowAsAttributeNames This operators uses the values of the specified row of the data set as new attribute names (including both regular and special columns).

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

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

class	AbsoluteDiscretization This operator discretizes all numeric attributes in the dataset into nominal attributes.
class	BinDiscretization This operator discretizes all numeric attributes in the dataset into nominal attributes.
class	DiscretizationModel The generic discretization model.
class	FrequencyDiscretization This operator discretizes all numeric attributes in the dataset into nominal attributes.
class	MinimalEntropyDiscretization This operator discretizes all numeric attributes in the dataset into nominal attributes.
class	MinMaxBinDiscretization This operator discretizes all numeric attributes in the dataset into nominal attributes.
class	UserBasedDiscretization This operator discretizes a numerical attribute to either a nominal or an ordinal attribute.

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

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

class	AbsoluteValueFilter This operator simply replaces all values by their absolute respective value.
class	AddNominalValue Adds a value to a nominal attribute definition.
class	AttributeAdd This operator creates a new attribute for the data set.
class	AttributeCopy Adds a copy of a single attribute to the given example set.
class	AttributeMerge This operator merges two attributes by simply concatenating the values and store those new values in a new attribute which will be nominal.
class	AttributeValueMapper This operator takes an ExampleSet as input and maps the values of certain attributes to other values.
class	AttributeValueReplace This operator creates new attributes from nominal attributes where the new attributes contain the original values which replaced substrings.
class	AttributeValueSplit This operator creates new attributes from a nominal attribute by dividing the nominal values into parts according to a split criterion (regular expression).
class	AttributeValueSubstring This operator creates new attributes from nominal attributes where the new attributes contain only substrings of the original values.
class	AttributeValueTrim This operator creates new attributes from nominal attributes where the new attributes contain the trimmed original values, i.e. leading and trailing spaces will be removed.
class	ChangeAttributeName This operator can be used to rename an attribute of the input example set.
class	ChangeAttributeNames2Generic This operator replaces the attribute names of the input example set by generic names like att1, att2, att3 etc.
class	ChangeAttributeNamesReplace This operator replaces parts of the attribute names (like whitespaces, parentheses, or other unwanted characters) by a specified replacement.
class	ChangeAttributeRole This operator can be used to change the attribute type of an attribute of the input example set.
class	ChangeAttributeType This operator can be used to change the attribute type of an attribute of the input example set.
class	Construction2Names This operator replaces the names of the regular attributes by the corresponding construction descriptions if the attribute was constructed at all.
class	Date2Nominal This operator transforms the specified date attribute and writes a new nominal attribute in a user specified format.
class	Date2Numerical This operator changes a date attribute into a numerical one.
class	DateAdjust
class	Dictionary Replaces strings
class	ExampleFilter This operator takes an ExampleSet as input and returns a new ExampleSet including only the Examples that fulfill a condition.
class	ExampleRangeFilter This operator keeps only the examples of a given range (including the borders).
class	ExampleSetToDictionary This operator takes two example sets and transforms the second into a dictionary.
class	ExchangeAttributeRoles This operator changes the attribute roles of two input attributes.
class	FeatureBlockTypeFilter This operator switches off all features whose block type matches the one given in the parameter skip_features_of_type.
class	FeatureFilter This is an abstract superclass for feature filters.
class	FeatureNameFilter This operator switches off all features whose name matches the one given in the parameter skip_features_with_name.
class	FeatureRangeRemoval This operator removes the attributes of a given range.
class	FeatureValueTypeFilter This operator switches off all features whose value type matches the one given in the parameter skip_features_of_type.
class	InfiniteValueReplenishment Replaces positive and negative infinite values in examples by one of the functions "none", "zero", "max_byte", "max_int", "max_double", and "missing".
class	InternalBinominalRemapping Correct internal mapping of binominal attributes according to the specified positive and negative values.
class	MergeNominalValues Merges two nominal values of a given regular attribute.
class	MissingValueImputation The operator MissingValueImpution imputes missing values by learning models for each attribute (except the label) and applying those models to the data set.
class	MissingValueReplenishment Replaces missing values in examples.
class	MissingValueReplenishmentView This operator simply creates a new view on the input data without changing the actual data or creating a new data table.
class	Nominal2Date This operator parses given nominal attributes in order to create date and / or time attributes.
class	Nominal2String Converts all nominal attributes to string attributes.
class	NominalNumbers2Numerical This operator transforms nominal attributes into numerical ones.
class	NominalToBinominal This operator maps the values of all nominal values to binary attributes.
class	NominalToBinominalModel This model maps the values of all nominal values to binary attributes.
class	NominalToNumeric This operator maps all non numeric attributes to real valued attributes.
class	NonDominatedSorting
class	Numerical2Real Converts all numerical attributes (especially integer attributes) to real valued attributes.
class	NumericToBinominal Converts all numerical attributes to binary ones.
class	NumericToFormattedNominal This operator tries to parse numerical values and formats them in the specified number format.
class	NumericToNominal Converts all numerical attributes to nominal ones.
class	NumericToPolynominal Converts all numerical attributes to nominal ones.
class	PermutationOperator This operator creates a new, shuffled ExampleSet by making a new copy of the exampletable in main memory!
class	Real2Integer Converts all real valued attributes to integer valued attributes.
class	RemoveDuplicates This operator removed duplicates from an example set by comparing all examples with each other on basis of the specified attributes.
class	SetData This operator simply sets the value for the specified example and attribute to the given value.
class	Sorting This operator sorts the given example set according to a single attribute.
class	String2Nominal Converts all string attributes to nominal attributes.
class	TFIDFFilter This operator generates TF-IDF values from the input data.
class	ValueReplenishment Abstract superclass for all operators that replenish values, e.g. nan or infinite values.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.filter.attributes

Classes in com.rapidminer.operator.preprocessing.filter.attributes that implement LoggingHandler

class

AttributeFilter This operator filters the attributes of an exampleSet.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.join

Classes in com.rapidminer.operator.preprocessing.join that implement LoggingHandler

class	AbstractExampleSetJoin Build the join of two example sets.
class	ExampleSetCartesian Build the cartesian product of two example sets.
class	ExampleSetIntersect This operator performs a set intersect on two example sets, i.e.
class	ExampleSetJoin Build the join of two example sets using the id attributes of the sets, i.e. both example sets must have an id attribute where the same id indicate the same examples.
class	ExampleSetMerge This operator merges two or more given example sets by adding all examples in one example table containing all data rows.
class	ExampleSetMinus This operator performs a set minus on two example sets, i.e.
class	ExampleSetSuperset This operator gets two example sets and adds new features to each of both example sets so that both example sets consist of the same set of features.
class	ExampleSetUnion This operator performs two steps: first, it build the union set / superset of features of both input example sets where common features are kept and both feature sets are extended in a way that the feature sets are equal for both example sets.

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

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

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	Normalization This operator performs a normalization.
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 LoggingHandler in com.rapidminer.operator.preprocessing.outlier

Classes in com.rapidminer.operator.preprocessing.outlier that implement LoggingHandler

class	AbstractOutlierDetection Abstract superclass of outlier detection operators.
class	DBOutlierOperator This operator is a DB outlier detection algorithm which calculates the DB(p,D)-outliers for an ExampleSet passed to the operator.
class	DKNOutlierOperator This operator performs a D^k_n Outlier Search according to the outlier detection approach recommended by Ramaswamy, Rastogi and Shim in "Efficient Algorithms for Mining Outliers from Large Data Sets".
class	LOFOutlierOperator This operator performs a LOF outlier search.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.sampling

Classes in com.rapidminer.operator.preprocessing.sampling that implement LoggingHandler

class	AbsoluteSampling Absolute sampling operator.
class	AbsoluteStratifiedSampling Stratified sampling operator.
class	AbstractBootstrapping This operator constructs a bootstrapped sample from the given example set.
class	AbstractSamplingOperator Abstract superclass of operators leaving the attribute set and data unchanged but reducing the number of examples.
class	AbstractStratifiedSampling Abstract superclass of stratified sampling operators.
class	Bootstrapping This operator constructs a bootstrapped sample from the given example set.
class	KennardStoneSampling This operator performs a Kennard-Stone Sampling.
class	ModelBasedSampling Sampling based on a learned model.
class	PartitionOperator Divides a data set into the defined partitions and deliver the subsets.
class	RatioStratifiedSampling Stratified sampling operator.
class	SimpleSampling Simple sampling operator.
class	WeightedBootstrapping This operator constructs a bootstrapped sample from the given example set which must provide a weight attribute.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.series

Classes in com.rapidminer.operator.preprocessing.series that implement LoggingHandler

class	AbstractSeriesProcessing This is the abstract superclass for all series processing operators.
class	EnsureMonotonicity This operator filters out all examples which would lead to a non-monotonic behaviour of the specified attribute.
class	FillDataGaps This operator fills gaps in the data based on the ID attribute of the data set.
class	LabelTrend2Classification This operator iterates over an example set with numeric label and converts the label values to either the class 'up' or the class 'down' based on whether the change from the previous label is positive or negative.
class	MultivariateSeries2WindowExamples This operator transforms a given example set containing series data into a new example set containing single valued examples.
class	Series2WindowExamples This is the superclass for all series to example transformation operators based on windowing.
class	SingleAttributes2ValueSeries Transforms all regular attributes of a given example set into a value series.
class	UnivariateSeries2WindowExamples This operator transforms a given example set containing series data into a new example set containing single valued examples.
class	WindowExamples2ModelingData This operator performs several transformations related to time series predictions based on a windowing approach.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.series.filter

Classes in com.rapidminer.operator.preprocessing.series.filter that implement LoggingHandler

class	CumulateSeries Generates a cumulative series from another series.
class	DifferentiateSeries This operator extracts changes from a numerical time series by comparing actual series values with past (lagged) values.
class	ExponentialSmoothing Creates a new series attribute which contains the original series exponentially smoothed.
class	IndexSeries Creates an index series from an original series.
class	LagSeries Adds lagged series attributes for the specified attributes.
class	MovingAverage Creates a new series attribute which contains the moving average of a series.
class	SeriesMissingValueReplenishment Replaces missing values in time series.
class	Trend Adds a trend line for a specified series attributes by regressing a dummy variable onto the series attribute.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.transformation

Classes in com.rapidminer.operator.preprocessing.transformation that implement LoggingHandler

class	AggregationOperator This operator creates a new example set from the input example set showing the results of arbitrary aggregation functions (as SUM, COUNT etc. known from SQL).
class	Attribute2ExamplePivoting This operator converts an example set by dividing examples which consist of multiple observations (at different times) into multiple examples, where each example covers on point in time.
class	Example2AttributePivoting Transforms an example set by grouping multiple examples of single groups into single examples.
class	ExampleSetTransformationOperator The abstract superclass for example set transformations.
class	GroupedANOVAOperator This operator creates groups of the input example set based on the defined grouping attribute.

Uses of LoggingHandler in com.rapidminer.operator.preprocessing.weighting

Classes in com.rapidminer.operator.preprocessing.weighting that implement LoggingHandler

class

EqualLabelWeighting This operator distributes example weights so that all example weights of labels sum up equally.

Uses of LoggingHandler in com.rapidminer.operator.similarity

Classes in com.rapidminer.operator.similarity that implement LoggingHandler

class	ExampleSet2Similarity This class represents an operator that creates a similarity measure based on an ExampleSet.
class	ExampleSet2SimilarityExampleSet This operator creates a new data set from the given one based on the specified similarity.
class	Similarity2ExampleSet This operator creates an example set from a given similarity measure.
class	SimilarityMeasure This is a wrapper around a DistanceMeasure.

Uses of LoggingHandler in com.rapidminer.operator.text

Classes in com.rapidminer.operator.text that implement LoggingHandler

class	SingleTextObjectInput This operator allows to create a TextObject filled with the text of the parameter text.
class	TextCleaner This operator allows to remove parts of a TextObject matching a given regular expression.
class	TextExtractor This operator allows to extract a part of a text using regular expressions.
class	TextObject This object encapsulates arbitrary text together with a label if applicable.
class	TextObject2ExampleSet This operator generates an exampleSet from a given input TextObject by creating a new exampleSet with a nominal attribute storing the text.
class	TextObjectLoader This operator loads a textObject from a textFile.
class	TextObjectWriter This operator writes a given textObject into a file.
class	TextSegmenter This operator segments a text based on a starting and ending regular expression.

Uses of LoggingHandler in com.rapidminer.operator.validation

Classes in com.rapidminer.operator.validation that implement LoggingHandler

class	AbstractBootstrappingValidation This validation operator performs several bootstrapped samplings (sampling with replacement) on the input set and trains a model on these samples.
class	BatchSlidingWindowValidation The BatchSlidingWindowValidation is similar to the usual SlidingWindowValidation.
class	BatchXValidation BatchXValidation encapsulates a cross-validation process.
class	BootstrappingValidation This validation operator performs several bootstrapped samplings (sampling with replacement) on the input set and trains a model on these samples.
class	CFSFeatureSetEvaluator CFS attribute subset evaluator.
class	ConsistencyFeatureSetEvaluator Consistency attribute subset evaluator.
class	FixedSplitValidationChain A FixedSplitValidationChain splits up the example set at a fixed point into a training and test set and evaluates the model (linear sampling).
class	IteratingPerformanceAverage This operator chain performs the inner operators the given number of times.
class	RandomSplitValidationChain A RandomSplitValidationChain splits up the example set into a training and test set and evaluates the model.
class	RandomSplitWrapperValidationChain This operator evaluates the performance of feature weighting algorithms including feature selection.
class	SlidingWindowValidation This is a special validation chain which can only be used for series predictions where the time points are encoded as examples.
class	ValidationChain Abstract superclass of operator chains that split an ExampleSet into a training and test set and return a performance vector.
class	WeightedBootstrappingValidation This validation operator performs several bootstrapped samplings (sampling with replacement) on the input set and trains a model on these samples.
class	WrapperValidationChain This operator evaluates the performance of feature weighting algorithms including feature selection.
class	WrapperXValidation This operator evaluates the performance of feature weighting and selection algorithms.
class	XValidation XValidation encapsulates a cross-validation process.

Uses of LoggingHandler in com.rapidminer.operator.validation.clustering

Classes in com.rapidminer.operator.validation.clustering that implement LoggingHandler

class	CentroidBasedEvaluator An evaluator for centroid based clustering methods.
class	ClusterDensityEvaluator This operator is used to evaluate a non-hierarchical cluster model based on the average within cluster similarity/distance.
class	ClusterNumberEvaluator This operator does actually not compute a performance criterion but simply provides the number of cluster as a value.
class	TestEvaluator Evaluates a cluster model by returning a random value.

Uses of LoggingHandler in com.rapidminer.operator.validation.clustering.exampledistribution

Classes in com.rapidminer.operator.validation.clustering.exampledistribution that implement LoggingHandler

class

ExampleDistributionEvaluator Evaluates flat cluster models on how well the examples are distributed over the clusters.

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

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

class	AnovaSignificanceTestOperator Determines if the null hypothesis (all actual mean values are the same) holds for the input performance vectors.
class	SignificanceTestOperator Determines if the null hypothesis (all actual mean values are the same) holds for the input performance vectors.
class	TTestSignificanceTestOperator Determines if the null hypothesis (all actual mean values are the same) holds for the input performance vectors.
static class	TTestSignificanceTestOperator.TTestSignificanceTestResult The result for a paired t-test.

Uses of LoggingHandler in com.rapidminer.operator.visualization

Classes in com.rapidminer.operator.visualization that implement LoggingHandler

class	ClearProcessLog This operator can be used to clear a data table generated by a ProcessLogOperator.
class	Data2Log This operator can be used to log a specific value of a given example set into the provided log value "data_value" which can then be logged by the operator ProcessLogOperator.
class	DatabaseExampleVisualizationOperator Queries the database table for the row with the requested ID and creates a generic example visualizer.
class	DataStatistics This class encapsulates some very simple statistics about the given attributes.
class	DataStatisticsOperator This operators calculates some very simple statistics about the given example set.
class	ExampleVisualizationOperator Remembers the given example set and uses the ids provided by this set for the query for the corresponding example and the creation of a generic example visualizer.
class	FormulaExtractor This operator extracts a prediction calculation formula from the given model and stores the formula in a formula result object which can then be written to a file, e.g. with the ResultWriter operator.
class	LiftChartGenerator This operator creates a Lift chart for the given example set and model.
class	LiftParetoChart This object can usually not be passed to other operators but can simply be used for the inline visualization of a Lift Pareto chart (without a dialog).
class	LiftParetoChartGenerator This operator creates a Lift chart based on a Pareto plot for the discretized confidence values for the given example set and model.
class	Macro2Log This operator can be used to log the current value of the specified macro.
class	ProcessLog2ExampleSet This operator transforms the data generated by a ProcessLog operator into an ExampleSet which can then be used by other operators.
class	ProcessLogOperator This operator records almost arbitrary data.
class	ROCBasedComparisonOperator This operator uses its inner operators (each of those must produce a model) and calculates the ROC curve for each of them.
class	ROCChartGenerator This operator creates a ROC chart for the given example set and model.
class	ROCComparison This object can usually not be passed to other operators but can simply be used for the inline visualization of a ROC comparison plot (without a dialog).
class	SOMModelVisualization This class provides an operator for the visualization of arbitrary models with help of the dimensionality reduction via a SOM of both the data set and the given model.

Uses of LoggingHandler in com.rapidminer.operator.visualization.dependencies

Classes in com.rapidminer.operator.visualization.dependencies that implement LoggingHandler

class	AbstractPairwiseMatrixOperator This operator calculates a dependency matrix between all attributes of the input example set.
class	ANOVAMatrix Displays the result of an ANOVA matrix calculation.
class	ANOVAMatrixOperator This operator calculates the significance of difference for the values for all numerical attributes depending on the groups defined by all nominal attributes.
class	CorrelationMatrixOperator This operator calculates the correlation matrix between all attributes of the input example set.
class	CovarianceMatrixOperator This operator calculates the covariances between all attributes of the input example set and returns a covariance matrix object which can be visualized.
class	MutualInformationMatrixOperator This operator calculates the mutual information matrix between all attributes of the input example set.
class	NumericalMatrix A simple (symmetrical) matrix which can be used for correlation or covariance matrices.
class	RainflowMatrix The Rainflow Matrix adds another data table view for the residuals of the Rainflow Matrix calculation as well as a new plot tab for the residuals.
class	RainflowMatrixOperator This operator calculates the rainflow matrix for a series attribute.
class	TransitionGraph This is the result of the TransitionGraphOperator, i.e. a graph representing connections between items (can be used for network visualizations).
class	TransitionGraphOperator This operator creates a transition graph from the given example set.
class	TransitionMatrixOperator This operator calculates the transition matrix of a specified attribute, i.e. the operator counts how often each possible nominal value follows after each other.

Uses of LoggingHandler in com.rapidminer.tools

Classes in com.rapidminer.tools that implement LoggingHandler

class

LogService Utility class providing static methods for logging.

Uses of LoggingHandler in com.rapidminer.tools.att

Methods in com.rapidminer.tools.att with parameters of type LoggingHandler

static AttributeDataSources	AttributeDataSource.createAttributeDataSources(java.io.File attributeDescriptionFile, boolean sourceColRequired, LoggingHandler logging) Returns a list of AttributeDataSources read from the file.
void	AttributeDataSourceCreator.loadData(java.io.File file, char[] commentChars, java.lang.String columnSeparators, char decimalPointCharacter, boolean useQuotes, char quoteChar, char escapeChar, boolean trimLines, boolean firstLineAsNames, int maxCounter, boolean skipErrorLines, java.nio.charset.Charset encoding, LoggingHandler logging)

Constructors in com.rapidminer.tools.att with parameters of type LoggingHandler

AttributeSet(java.io.File attributeDescriptionFile, boolean sourceColRequired, LoggingHandler logging)
Reads an xml attribute description file and creates an attribute set.

Uses of LoggingHandler in com.rapidminer.tools.jdbc

Methods in com.rapidminer.tools.jdbc with parameters of type LoggingHandler

static DatabaseHandler

DatabaseHandler.getConnectedDatabaseHandler(java.lang.String databaseURL, java.lang.String username, java.lang.String password, JDBCProperties properties, LoggingHandler logging) Returns a connected database handler instance from the given connection data.

Uses of LoggingHandler in com.rapidminer.tools.math

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

static class	AnovaCalculator.AnovaSignificanceTestResult
class	Averagable Superclass for all objects which can be averaged.
class	AverageVector Handles several averagables.
class	RunVector Collects the average vectors of a run.
class	SignificanceTestResult This class encapsulates the result of a statistical significance test.

Uses of LoggingHandler in com.rapidminer.tools.math.optimization.ec.es

Constructors in com.rapidminer.tools.math.optimization.ec.es with parameters of type LoggingHandler

ESOptimization(double[] minValues, double[] maxValues, int populationSize, int individualSize, int initType, int maxGenerations, int generationsWithoutImprovement, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double defaultSigma, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

ESOptimization(double minValue, double maxValue, int populationSize, int individualSize, int initType, int maxGenerations, int generationsWithoutImprovement, int selectionType, double tournamentFraction, boolean keepBest, int mutationType, double crossoverProb, boolean showConvergencePlot, boolean showPopulationPlot, RandomGenerator random, LoggingHandler logging)
Creates a new evolutionary SVM optimization.

VarianceAdaption(GaussianMutation mutation, int intervalSize, LoggingHandler logging)
The interval size should be as big as the changeable components, i.e. the number of examples (alphas).