All Classes

Class	Description
AArffLoader	Safe version of the ArffLoader, always retaining string values.
AArffLoader.AArffReader
AbsolutePredictionErrorComparator	Comparator for predictions using the (absolute) prediction error (sorting increasingly).
AbstainAttributePercentile	Only predict if attribute value within percentile range.
AbstainAverage	Average base classifiers, abstain if difference outside thresholds Valid options are:
AbstainAverageWithClassifierWeights	Average base classifiers, abstain if difference outside thresholds Valid options are:
AbstainingCascade	The specified classifiers represent a cascade: if the first one abstains, the second is used (and so on), otherwise the prediction is returned. If all classifiers prior to the last one abstained then the prediction of the last one is returned.
AbstainingClassifier	Interface for classifiers that may support abstaining.
AbstainingClassifierWrapper	Wraps an abstaining classifier and allows turning on/of abstaining.
AbstainingLWL	LWL variant that supports abstaining if the base classifier is able to.
AbstainLeastMedianSq	Finds the base classifier with the best least median squared error.
AbstainMinimumProbability	Abstains if the probability of the chosen class label is below the specified threshold.
AbstainVote	Finds the base classifier with the best least median squared error.
AbstractAdamsExperimentIO<T extends AbstractExperiment>	Ancestor for classes that handle loading/saving of experiments.
AbstractAdamsExperimentReader	Ancestor for readers for ADAMS Experiments.
AbstractAdamsExperimentRunner<T extends AbstractExperiment>	Ancestor for classes that handle running a copy of the experiment in a separate thread.
AbstractAdamsExperimentWriter	Ancestor for ADAMS Experiment writers.
AbstractAdamsSetupPanel	Ancestor for setup panels for ADAMS experiments.
AbstractAdditionalExplorerPanel	Wrapper class for additional panels to be displayed in the Explorer.
AbstractAnalysisPanel	Ancestor for panels that analysis experimental results.
AbstractAssociatorEvaluation	Ancestor for associator evaluation setups.
AbstractAttributeCapabilities	Ancestor for capabilities-based conditions.
AbstractAttributeSelectionEvaluation	Ancestor for attribute selection evaluation setups.
AbstractCallableWekaClassifierEvaluator	Ancestor for classifier evaluators that make use of a callable classifier.
AbstractCallableWekaClustererEvaluator	Ancestor for clusterer evaluators that make use of a callable clusterer.
AbstractClassAttributeHeuristic	Ancestor for heuristics that determine the class attribute for a dataset.
AbstractClassifierBasedGeneticAlgorithm	Ancestor for genetic algorithms that evaluate classifiers.
AbstractClassifierBasedGeneticAlgorithm.ClassifierBasedGeneticAlgorithmJob<T extends AbstractClassifierBasedGeneticAlgorithm>	Job class for algorithms with datasets.
AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation	Ancestor for genetic algorithms that offer a second evaluation using a different seed value.
AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation.ClassifierBasedGeneticAlgorithmWithSecondEvaluationJob<T extends AbstractClassifierBasedGeneticAlgorithmWithSecondEvaluation>	Job class for algorithms with datasets.
AbstractClassifierBasedGeneticAlgorithmWizard	Ancestor for optimizing datasets (attribute selection) using a genetic algorithm.
AbstractClassifierBasedGeneticAlgorithmWizard.PerformancePlot	For plotting the performance of the genetic algorithm.
AbstractClassifierEvaluation	Ancestor for classifier evaluation setups.
AbstractClassifierSetupProcessor	Ancestor for schemes that preprocess classifier arrays.
AbstractClustererEvaluation	Ancestor for clusterer evaluation setups.
AbstractClustererPostProcessor	Ancestor for post-processors for output that the WekaClusterer transformer produces.
AbstractClusterMembershipPostProcessor	Ancestor for post-processors that require a built clusterer and the dataset that was used to build the clusterer to be present in the model container.
AbstractColumnFinder	Ancestor for classes that find columns of interest in datasets.
AbstractColumnFinderApplier	Ancestor for filters that apply ColumnFinder schemes to the data.
AbstractColumnFinderWithCapabilities	Ancestor for classes that find columns of interest in datasets.
AbstractCommunicationProcessor	Ancestor for classes processing the communication to/fro Pyro proxy models.
AbstractCrossvalidatedInstanceEvaluator<T extends AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer>	Ancestor for evalutors that use cross-validation for initialization.
AbstractCrossvalidatedInstanceEvaluator.EvaluationContainer	Container for storing the evaluation results.
AbstractDataContainer	Ancestor for data containers.
AbstractDataPreparation	Ancestor for classes that prepare data for the SocketFacade classifier.
AbstractDatasetInstanceEvaluator	Ancestor for evaluators that need a data set for initialization.
AbstractDetrend	Ancestor for schemes that perform detrend.
AbstractEditableDataTableAction	Ancestor for actions on the data displayed on a tab using a AbstractInvestigatorTabWithEditableDataTable.
AbstractErrorScaler	Ancestor for classes that scale predictions.
AbstractEvaluation<T extends AbstractInvestigatorTab,R extends AbstractResultItem>	Ancestor for evaluation setups.
AbstractExperiment	Ancestor for simple experiments.
AbstractExperiment.AbstractExperimentJob<T extends AbstractExperiment>	For evaluating a single classifier/dataset combination.
AbstractExperimenterPanel	Ancestor for panels in the experimenter.
AbstractExperimentIO<T>	Ancestor for classes that handle loading/saving of experiments.
AbstractExperimentRunner<T>	Ancestor for classes that handle running a copy of the experiment in a separate thread.
AbstractExperimentSetup	Ancestor for experiment setups.
AbstractExplorerPanelHandler	Ancestor for handlers for specific Explorer panels.
AbstractFilteredColumnFinder	Ancestor for column finders that pre-filter the columns.
AbstractFilteredRowFinder	Ancestor for row finders that pre-filter the rows.
AbstractFinalModelGenerator	.
AbstractGeneticAlgorithm	Base class for genetic algorithms.
AbstractGeneticDoubleMatrixDiscoveryHandler	Ancestor for genetic discovery handlers that handle matrix properties.
AbstractHashableInstance	Ancestor for instance classes that wraps around any WEKA Instance and allow them to be used in data structures that make use of on object's hash, like maps or hashtables.
AbstractHistoryPopupMenuItem<H extends adams.gui.core.AbstractNamedHistoryPanel,T extends AbstractInvestigatorTab>	Ancestor for classes that add menu items to the history popup menu.
AbstractHistoryPopupMenuItem	Ancestor for classes that add menu items to the history popup menu.
AbstractHistoryPopupMenuItem	Ancestor for classes that add menu items to the history popup menu.
AbstractHistoryPopupMenuItem	Ancestor for classes that add menu items to the history popup menu.
AbstractHistoryPopupMenuItem	Ancestor for classes that add menu items to the history popup menu.
AbstractInstanceEvaluator	Ancestor for evaluators that evaluate weka.core.Instance objects.
AbstractInstanceGenerator<T extends adams.data.container.DataContainer & adams.data.report.ReportHandler>	Abstract base class for schemes that turn temperature profiles into weka.core.Instance objects.
AbstractInstanceGenerator<T extends adams.data.container.DataContainer>	Ancestor for transformers that turn data containers into WEKA Instance objects.
AbstractInstanceInfoFrame	Ancestor for frames for displaying information on the displayed data, with some more domain-specific functionality.
AbstractInstancePaintlet	Ancestor for Instance paintlets.
AbstractInstancePanelUpdater	Ancestor for classes that determine when to update the instance panel, i.e., repaint all of it.
AbstractInstancesAnalysis	Ancestor for data analysis classes.
AbstractInstancesIndexedSplitsRunsGenerator	Ancestor for generators that process Instances objects.
AbstractInvestigatorTab	Ancestor for tabs in the Investigator.
AbstractInvestigatorTab.SerializationOption	options for serialization.
AbstractInvestigatorTabWithDataTable	Ancestor for tabs that have the data table on top.
AbstractInvestigatorTabWithEditableDataTable	Ancestor for tabs with modifiable data table.
AbstractLinearRegressionBased<T extends adams.data.container.DataContainer>	Abstract ancestor for linear regression based baseline correction schemes.
AbstractMatchWekaInstanceAgainstHeader	Ancestor for classes that match Instance objects against Instances headers.
AbstractMerge
AbstractMultiClassPLS	Ancestor for schemes that predict multiple classes.
AbstractMultiplicativeScatterCorrection	Ancestor for correction schemes.
AbstractMultiRowProcessorPlugin	Ancestor for MultiRowProcessor plugins.
AbstractNestableResultItem	Container for a data to be stored in result history that can also store nested result items.
AbstractNumericClassPostProcessor	Ancestor for numeric class post-processors.
AbstractOutputGenerator<T extends AbstractResultItem>	Ancestor for output generators.
AbstractOutputGenerator	Ancestor for output generators using t.
AbstractOutputGenerator	Ancestor for output generators using t.
AbstractOutputGenerator	Ancestor for output generators using the data from the per-fold pane.
AbstractOutputGenerator	Ancestor for output generators using t.
AbstractOutputGenerator	Ancestor for output generators using the data from the per-fold pane.
AbstractOutputGeneratorWithSeparateFoldsSupport<T extends JComponent>	Ancestor for output generators that can generate output for separate folds just using the Evaluation objects.
AbstractOutputPanel	Ancestor for panels that allow the user to configure ResultListeners.
AbstractOutputPanelWithPopupMenu<T extends adams.gui.chooser.BaseFileChooser>	Ancestor for output panels that can save the displayed output to a file.
AbstractPanelWithFile<T extends adams.gui.chooser.AbstractChooserPanel>	Ancestor for panels that allow the user to select a file.
AbstractParameterHandlingWekaMenuItemDefinition	Abstract menu item definition for Weka elements that also handle additional parameters.
AbstractPerFoldPopupMenuItem	Ancestor for classes that add menu items to the per-fold popup menu.
AbstractPlotColumn	Ancestor for plugins that plot a column.
AbstractPlotRow	Ancestor for plugins that plot a row.
AbstractPlotSelectedRows	Ancestor for plugins that plot rows.
AbstractPLS	Ancestor for PLS implementations.
AbstractPLSAttributeEval	Ancestor for PLS attribute evaluators
AbstractPLSAttributeEval.LoadingsCalculations
AbstractProcessCell	Ancestor for plugins that process a cell.
AbstractProcessColumn	Ancestor for plugins that process a column.
AbstractProcessRow	Ancestor for plugins that process a row.
AbstractProcessSelectedRows	Ancestor for plugins that process a row.
AbstractProcessWekaInstanceWithModel<T>	Ancestor for transformers that user models for processing Instance objects, e.g., classifiers making predictions.
AbstractRangeBasedSelectionProcessor	Ancestor for processors that work on a range of attributes.
AbstractRelationNameHeuristic	Ancestor for heuristics that determine the relation name for a dataset.
AbstractResultItem	Container for a data to be stored in result history.
AbstractResultsHandler	Ancestor for classes that store the results from an experiment run.
AbstractResultsPanel	Ancestor for displaying the results of an analysis.
AbstractRowFinder	Ancestor for classes that find rows of interest in datasets.
AbstractRowFinderApplier	Ancestor for filters that apply RowFinder schemes to the data.
AbstractRowFinderWithCapabilities	Ancestor for classes that find rows of interest in datasets.
AbstractRowSelection	Ancestor for row selection schemes.
AbstractSelectedAttributesAction	Ancestor for actions on ther checked attributes in the PreprocessTab.
AbstractSelectionProcessor	Ancestor for row selection processors.
AbstractSetupOptionPanel	Ancestor for panels that get added to a AbstractSetupPanel.
AbstractSetupPanel<T>	Ancestor for setup panels.
AbstractSimpleClassifier	Ancestor for classifiers using ADAMS option handling.
AbstractSimpleOptionHandler	Ancestor for Weka classes that use the ADAMS option handling framework.
AbstractSimpleRegressionMeasure	Computes the mean error.
AbstractSingleClassPLS	Ancestor for schemes that predict a single class.
AbstractSource	Ancestor for additional "source" actions in the main menu.
AbstractSplitGenerator	Ancestor for helper classes that generates dataset splits.
AbstractSplitter	Parent class for different methods of splitting a dataset into smaller datasets.
AbstractTokenCleaner	Ancestor for cleaning tokens.
AbstractTrainableColumnFinder	Ancestor for ColumnFinder algorithms that can be trained.
AbstractTrainableRowFinder	Ancestor for RowFinder algorithms that can be trained.
AbstractWekaClassifierEvaluator	Ancestor for transformers that evaluate classifiers.
AbstractWekaEnsembleGenerator	Ancestor for schemes that generate ensembles.
AbstractWekaEvaluationPostProcessor	Ancestor for classes that post-process Evaluation objects.
AbstractWekaExperimentIO<T extends weka.experiment.Experiment>	Ancestor for classes that handle loading/saving of experiments.
AbstractWekaExperimentRunner<T extends weka.experiment.Experiment>	Ancestor for classes that handle running a copy of the experiment in a separate thread.
AbstractWEKAFitnessFunction	Perform attribute selection using WEKA classification.
AbstractWEKAFitnessFunction.Measure	The measure to use for evaluating.
AbstractWekaMenuItemDefinition	Abstract menu item menu item definitions for Weka elements.
AbstractWekaModelReader	Ancestor for actors that deserialize models.
AbstractWekaModelWriter	Ancestor for actors that serialize models.
AbstractWekaPackageManagerAction	Ancestor for package manager actions.
AbstractWekaPackageManagerAction	Ancestor for package manager actions.
AbstractWekaPackageManagerAction	Ancestor for package manager actions.
AbstractWekaPredictionsTransformer	Ancestor for transformers that convert the predictions stored in an Evaluation object into a different format.
AbstractWekaRepeatedCrossValidationOutput<T>	Ancestor for classes that generate output from repeated cross-validations.
AbstractWekaSetupGenerator<T>	Abstract ancestor for setup generator sources.
AbstractWekaSetupPanel	Ancestor for setup panels for Weka experiments.
AbstractWekaSpreadSheetReader	Ancestor for WEKA file format readers.
AbstractWekaSpreadSheetWriter	Ancestor for WEKA file format readers.
AccumulatedLWLWeights	Generates an LWL-like dataset for each instance of the data from the first batch and accumulate these weights.
AdamsExperimentFileChooser	A specialized JFileChooser that lists all available file Readers and Writers for ADAMS Experiments.
AdamsExperimentRunner<T extends AbstractExperiment>	Ancestor for classes that handle running a copy of the experiment in a separate thread.
AdamsHelper	Helper class to make Weka GUI more ADAMS-like.
AdamsInstanceCapabilities	Filters adams.data.instance.Instance based on defined capabilities.
AdamsInstanceToWekaInstance	Converts adams.data.instance.Instance objects into weka.core.Instance ones.
AddCluster	Just adds the predicted cluster (or distribution) to the original dataset. Stored in container under: Clustered dataset
AdditionalExplorerPanel	Interface for classes that supply additional Explorer panels.
Adjust	Whether to adjust the plot to the loaded or visible data.
AggregateEvaluations	Allows the aggregation of Evaluation objects.
AlignDataset	Aligns the dataset(s) passing through to the reference dataset. Makes use of the following other filters internally: - weka.filters.unsupervised.attribute.AnyToString - weka.filters.unsupervised.instance.RemoveWithLabels Valid options are:
AllFinder	Dummy finder, finds all columns.
AllFinder	Dummy finder, finds all rows.
AnalysisPanel	The analysis panel.
AndrewsCurves	Generates Andrews Curves from array data. César Ignacio García Osorio, Colin Fyfe (2003).
AnyToString	Turns the selected range of attributes into string ones.
Append	Appends the selected datasets into single dataset (one-after-the-other).
AppendDatasets	For appending datasets into single dataset.
AppendDatasetsPanel	Wizard panel that allows appending datasets (one after the other).
ArffOutputPanel	Stores the results in an ARFF file.
ArffSpreadSheetReader	Reads WEKA datasets in ARFF format and turns them into spreadsheets.
ArffSpreadSheetWriter	Writes a spreadsheet in ARFF file format.
ArffUtils	A helper class for ARFF related stuff.
ArffViewer	Opens the ARFF viewer.
ArrayStatistic	Allows the calculation of row statistics.
AssociateTab	Tab for associators.
AssociateTab.HistoryPanel	Customized history panel.
AssociationsHandler	Manages the AssociationsPanel.
AttributeIndex	Uses the supplied attribute index to select the class attribute.
AttributeIndex	Uses the name of the specified attribute as relation name.
AttributeSelection	Perform attribute selection using WEKA classification.
AttributeSelectionHandler	Manages the AttributeSelectionPanel.
AttributeSelectionPanel	Creates a panel that displays the attributes contained in a set of instances, letting the user toggle whether each attribute is selected or not (eg: so that unselected attributes can be removed before classification).
AttributeSelectionPanel.AttributeTableModel	A table model that looks at the names of attributes and maintains a list of attributes that have been "selected".
AttributeSelectionTab	Tab for attribute selection.
AttributeSelectionTab.HistoryPanel	Customized history panel.
AttributeStatistics	Displays statistics for the selected attribute.
AttributeSummaryPanel	This panel displays summary statistics about an attribute: name, type number/% of missing/unique values, number of distinct values.
AttributeSummaryPanel.AttributeInfoPanel	Panel with labels displaying some basic info.
AttributeSummaryPanel.StatisticsTable	Displays other stats in a table.
AttributeSummaryTransferFilter	Filter which trains another filter to summarise a sub-set of the data's attributes.
AttributeTypeList	Wrapper for a comma-separated list of attribute types.
AttributeValueCellRenderer	Handles the background colors for missing values differently than the DefaultTableCellRenderer.
AttributeVisualizationPanel	Creates a panel that shows a visualization of an attribute in a dataset.
AutoScaler	Applies the specified numeric scaler to the data in case of a numeric class attribute, otherwise just passes on the data.
Average	Computes the average of the numeric attributes defined in the range.
AverageSilhouetteCoefficient	Computes the average Silhouette coefficient for the clusters.
BasicAdamsSetupPanel	Basic interface for setting up an ADAMS experiment.
BasicWekaSetupPanel	Basic interface for setting up a Weka experiment.
BatchFilterDatasets	For batch filtering datasets using a single filter setup (files get output into a different directory).
BatchFilterDatasetsPanel	Wizard panel that allows appending datasets (one after the other).
BayesNetEditor	Opens the BayesNet Editor.
BestBinnedNumericClassRandomSplitGenerator	Picks the best binning algorithm from the provided ones.
Bias	Computes the bias (mean error) for regression models.
BinnableInstances	Helper class for binning instances.
BinnableInstances.ClassValueBinValueExtractor	Uses the class value as bin value.
BinnableInstances.GroupedClassValueBinValueExtractor	Uses the class value of the first instance in the group as bin value.
BinnableInstances.NumericClassGroupExtractor	Group extractor for numeric class attributes (using string representation of values).
BinnableInstances.StringAttributeGroupExtractor	Group extractor for string attributes.
BinnedNumericClassCrossValidationFoldGenerator	Helper class for generating cross-validation folds.
BinnedNumericClassRandomSplitGenerator	Generates random splits of datasets with numeric classes using a binning algorithm.
Binning	Allows to perform binning of the values from a column or row.
BoundaryVisualizer	Displays data in the boundary visualizer.
BoxPlotTab	Visualizes the selected dataset as box plot.
BuildModel	Builds a model and serializes it to a file.
BuildModel	Builds a model and serializes it to a file.
ByExactName	Returns indices of columns which names match the exact name.
ByExactName	Returns indices of columns which names match the exact name.
ByLabel	Returns the indices of rows which attributes labels match the provided regular expression.
ByName	The first attribute name that matches the regular expression is used as class attribute.
ByName	Returns indices of attributes which names match the regular expression.
ByNumericRange	Returns indices of rows which numeric value match the min/max.
ByNumericValue	Returns indices of rows which numeric value match the min/max.
Capability	Enumeration of all capabilities.
CenterStatistic	Enumeration of available center statistics.
ChangeAttributeWeight	Allows the user to change the weight of the selected attribute.
ChangeInstanceWeights	Allows the user to change the weight of the selected attribute.
Class	Column finder which finds the class column (if one is set).
ClassAttribute	Uses the class attribute name.
ClassesToClusters	Tries to map the clusters of the built clusterer to the class labels in the dataset.
ClassificationViaPLS	Performs ClassificationViaRegression using PLSClassifierWeightedWithLoadings as base classifier, allowing access to the PLS matrices.
ClassificationViaRegressionD	Class for doing classification using regression methods.
ClassifierCascade	Generates a classifier cascade, with each deeper level of classifiers being built on the input data and either the class distributions (nominal class) or classification (numeric class) of the classifiers of the previous level in the cascade. The build process is stopped when either the maximum number of levels is reached, the termination criterion is satisfied or no further improvement is achieved. In case of a level performing worse than the prior one, the build process is terminated immediately and the current level discarded.
ClassifierCascade.Combination	Defines how to combine the predictions of the final layer and turn it into actual predictions.
ClassifierCascade.ThresholdCheck	Defines how to check the threshold.
ClassifierErrors	Generates classifier errors plot.
ClassifierHandler	Manages the ClassifierPanel.
ClassifierPanel	Panel for listing datasets.
ClassifyTab	Tab for classification.
ClassifyTab.HistoryPanel	Customized history panel.
ClassRangeBasedClassifierErrors	Displays the classifier errors using Weka panels, but with a sizes adjusted to the class range.
Clipboard	Parses content on the clipboard.
ClusterAssignments	Displays the cluster assignments.
ClusterCenters	Computes the cluster centers for the provided dataset.
ClusterCounts	Creates an overview of how many instances get clustered into each cluster. Stored in container under: Clustered dataset
ClustererHandler	Manages the ClustererPanel.
ClusterStatistics	Computes cluster statistics (min/max/mean/stdev) for the provided dataset.
ClusterTab	Tab for clustering.
ClusterTab.HistoryPanel	Customized history panel.
ColumnFinder	Interface for classes that "find" columns of interest in datasets.
ColumnSplitter	Splits a dataset in two based on the columns selected by a column-finder.
ColumnStatistic	Allows the calculation of column statistics.
CompareDatasets	Compares two datasets, either row-by-row or using a row attribute listing a unique ID for matching the rows, outputting the correlation coefficient of the numeric attributes found in the ranges defined by the user. In order to trim down the number of generated rows, a threshold can be specified.
CompareModels	Compares the predictions of two models.
CompareTab	For comparing datasets.
Compatibility	Checks the compatibility of the selected datasets.
ComponentContentPanel	Panel for exporting the graphical component as image.
ConfusionMatrix	Displays the confusion matrix.
Consensus	Outputs predictions only if the ensemble agrees.
ConsensusOrVote	If the required minimum number of classifiers of the ensemble agree on a label, then this label is predicted.
Constant	Column finder that finds a constant set of columns.
Constant	Row finder that finds a constant set of rows.
ConvertToDate	Converts the selected string attributes to date ones.
ConvertToNominal	Converts the selected attributes to nominal ones.
ConvertToString	Converts the selected attributes to string ones.
Copy	Copies the selected dataset.
CopySetup	Simply copies the classifier setup to the clipboard.
Corr	Assume NO MISSING VALUES, all attributes must be NUMERIC (or 0/1 maybe ...).
CorrelationMatrix	Computes a matrix with the correlation coefficients between attributes.
CostCurve	Displays Cost curve data.
CostCurvePanel	Displays cost curve data.
CrossValidation	Performs cross-validation.
CrossValidation	Performs cross-validation.
CrossValidation	Performs cross-validation.
CrossValidationExperiment	Performs cross-validation.
CrossValidationExperiment.CrossValidationExperimentJob	Performs cross-validation on the classifier/data combination.
CrossValidationFoldGenerator	Interface for generating cross-validation folds.
CrossValidationHelper	Helper class for cross-validation.
CrossValidationSetup	Setup for a cross-validation experiment.
CsvOutputPanel	Stores the results in a CSV file.
CustomOutputPanel	Allows the user to configure any ResultListener.
DarkLord	For optimizing datasets (attribute selection) using genetic algorithm.
DarkLord
DarkLord.DarkLordJob	A job class specific to The Dark Lord.
Database	For loading data from a database.
DatabaseContainer	Dataset loaded from database.
DataCellView	Wrapper for single cell values in a Instance object.
DataContainer	Interface for data containers.
DataContainerList	For managing the data containers.
DataGenerator	For generating data using a data generator.
DataGeneratorContainer	Dataset generated by datagenerator.
DataQueryTab	Allows the execution of an SQL-like query to manipulate datasets.
DatasetCleaner	Removes all columns from the data data that have been indentified.
DatasetCleaner	Removes all rows from the data data that have been indentified.
DatasetCompatibility	For checking compatibility of datasets.
DatasetCompatibilityPanel	Compares the headers of a number of datasets and outputs the results.
DatasetFileChooserPanel	A panel that contains a text field with the current file and a button for bringing up a ConverterFileChooser.
DatasetHelper	Helper class for dealing with datasets.
DatasetLabeler	Adds an additional attribute to the dataset containing a label whether it was a match or not, i.e., whether the row finder selected a particular row or not.
DatasetPanel	Panel for listing datasets.
DataSort	Allows sorting the data using multiple columns.
DataTab	Lists the currently loaded datasets.
DataTable	Specialized table with custom cell editors for the class.
DataTableModel	Model for displaying the loaded data.
DataTableWithButtons	Specialized table with buttons.
DefaultAdamsExperimentIO	Default IO handler for experiments.
DefaultAnalysisPanel	Default panel for analyzing results from experiments.
DefaultAnalysisPanel.HistoryPanel	Customized history panel.
DefaultCrossValidationFoldGenerator	Helper class for generating cross-validation folds.
DefaultHandler	Dummy handler, in case no other handler was located for an explorer panel.
DefaultRandomSplitGenerator	Generates random splits of datasets.
DefaultWekaExperimentIO	Default IO handler for experiments.
DefaultWekaExperimentRunner	A class that handles running a copy of the experiment in a separate thread.
Detrend	Performs Detrend, using the specified correction scheme.
Dice	Sørensen–Dice coefficient: https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
DIPLS
DownSample	A simple filter that retains only every nth attribute.
EditWekaASEvaluator	Lets the user edit the Weka attribute selection evaluator.
EditWekaASSearch	Lets the user edit the Weka attribute selection search.
EditWekaClassifier	Lets the user edit the Weka classifier.
EditWekaClusterer	Lets the user edit the Weka clusterer.
EditWekaDataGenerator	Lets the user edit the Weka data generator.
EditWekaFilter	Lets the user edit the Weka filter.
EditWekaStreamableFilter	Lets the user edit the Weka filter.
EncloseClassifier	For enclosing classifiers in SingleClassifierEnhancer wrappers.
EncloseClusterer	For enclosing clusterers in SingleClustererEnhancer wrappers.
EquiDistance	A filter for interpolating the numeric attributes.Using the same number of points as are currently present in the input will have no effect.
EquiDistance.AttributeSelection	Defines how the attributes are selected.
EvaluationHelper	A helper class for Evaluation related things.
EvaluationStatistic	The enumeration for the comparison field.
Experimenter	Opens the WEKA Experimenter.
ExperimenterEntryPanel	Allows the display of multiple Experimenter panels.
ExperimenterPanel	The Experimenter panel.
ExperimentHandler	Manages the ExperimentPanel.
ExperimentPanel	This panel allows the user to select and configure a classifier, set the attribute of the current dataset to be used as the class, and perform an Experiment (like in the Experimenter) with this Classifier/Dataset combination.
ExperimentStatistic	The enumeration for the comparison field.
ExperimentTab	Tab for running experiment on selected dataset/classifier.
ExperimentTab.HistoryPanel	Customized history panel.
ExperimentWithCustomizableRelationNames	Interface for experiments that allow customizing the relation names of the datasets.
Explorer	Opens the WEKA Explorer.
ExplorerEntryPanel	Allows the display of multiple Explorer panels.
ExplorerExt	An extended Explorer interface using menus instead of buttons, as well as remembering recent files.
ExtExperiment	Extended version of the Weka Experiment class.
FakeClassifier	Fake classifier that requires no dataset for training and just outputs random values within the specified bounds. Fake build and prediction times can be set as well.
Fallback	In case the base classifier fails to make predictions, uses fallback one.
FastICA	Performs independent components analysis and allows access to components and sources.
FastWavelet	A filter for wavelet transformation using the JSci library's fast wavelet transform (FWT) algorithms. For more information see: (2009).
FFT	A filter that transforms the data with Fast Fourier Transform. Pads with zeroes. For more information see: Mark Hale (2009).
FileContainer	File-based dataset.
FileName	Suggests the file name (without extension) as the relation name.
FileResultsHandler	Writes the experiment results to a file.
FilteredClassifierExt	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
FilteredFilter	First applies the pre-filter to the data and the generated data is fed into the main filter.
FilteredIQR	Returns indices of rows that got identified as outliers/extreme values.
FilteredSearch	Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
FixedClassifierErrors	Displays the classifier errors using Weka panels, but with a fixed size of the error plots.
FixedClassifierErrorsPlot	Displays the classifier errors using an ADAMS plot with fixed size crosses.
FixedSizeErrorScaler	Scales the errors to a fixed size.
FlowFilter	Processes the data with a flow.
FourInOnePlot	Generates the 4-in-1 plot: normal plot, histogram, residuals vs fit and vs order.
FromPredictions	Loads predictions from a spreadsheet for evaluation.
FromPredictions	Encapsulates predictions from a spreadsheet.
FusionJsonCommunicationProcessor	Turns Instances/Instance into fusion JSON.
GaussianProcessesAdaptive	Implements Gaussian Processes for regression without hyperparameter-tuning.
GaussianProcessesNoWeights	* Implements Gaussian processes for regression without hyperparameter-tuning.
GaussianProcessesWeighted	Implements Gaussian Processes for regression without hyperparameter-tuning.
GenericDoubleResolution	Generic handler for double properties (using resolution).
GenericFloatResolution	Generic handler for float properties (using resolution).
GenericInteger	Generic handler for integer properties.
GenericPLSMatrixAccess	For classes that allow access to PLS matrices.
GenericString	Generic handler for string properties.
GeneticAlgorithm	Morticia (GEX).
GeneticAlgorithm	Applies the specified genetic algorithm to the training data and uses the best setup for the final model.
GeneticAlgorithm.GAJob	Class for multithreading the ga.
GPD	Implements Gaussian Processes for regression without hyperparameter-tuning, with an inline RBF kernel. For more information see David J.C.
GPDGamma	GPD gamma handler.
GPDNoise	GPD noise handler.
GraphHelper	Helper class for graphs.
GraphSource	Displays the source of a weka.core.Drawable graph.
GraphSource	Displays the source code of the graph (dot or XML BIF).
GraphSource	Displays the source code of the graph (dot or XML BIF).
GraphVisualizer	Displays data in the graph visualizer.
GraphVisualizer	Displays BayesNet graphs.
GroupedBinnedNumericClassCrossValidationFoldGenerator	Helper class for generating cross-validation folds.
GroupedBinnedNumericClassRandomSplitGenerator	Generates random splits of datasets with numeric classes using a binning algorithm.
GroupedCrossValidationFoldGenerator	Helper class for generating cross-validation folds.
GroupedCrossValidationFoldGeneratorUsingNumericClassValues	Helper class for generating cross-validation folds. Uses the string representation of the numeric class values as grouping.
GroupedRandomSplitGenerator	Generates random splits of datasets, making sure that groups of instances stay together (identified via a regexp).
GroupExpression	Identifies groups in strings using regular expressions.
HashableInstanceUsingString	TODO: what this class does
HashableInstanceUsingSum	Computes the hashcode as sum of the internal double values.
Hermione	For optimizing datasets (parameter selection) using genetic algorithm.
Hermione	Hermione.
Hermione.HermioneJob	A job class specific to Hermione.
HighLowSplit	Uses base classifier to get guess, then get prediction from either lo/hi classifier Valid options are:
HighLowSplitSingleClassifier	Uses base classifier to get guess, then get prediction from either lo/hi classifier Valid options are:
Histogram	Generates histograms from the visible containers.
Histogram	Allows to generate a histogram from a column or row.
HistogramFactory	A factory for histogram related objects.
HistogramFactory.Dialog	Dialog for displaying histograms generated from instances.
HistogramFactory.Panel	A panel for displaying a histogram based on the GC data of a instance.
HistogramFactory.SetupDialog	A dialog that queries the user about parameters for displaying histograms.
IndependentComponentsTab	Visualizes the ICA components/sources and ICA space calculated from the selected dataset.
IndexedSplitsRunsEvaluation	Performs the evaluation according to the provided indexed splits.
IndividualRows	Just selects each row by itself.
InputSmearing	Extended version of weka.classifiers.meta.Bagging, which allows input smearing of numeric attributes. Class for bagging a classifier to reduce variance.
InputSmearing
InstallFromFile	Action that installs packages from files.
InstallFromURL	Action that installs packages from URLs.
InstallOfficial	Action that installs official packages via their name and (optional) version.
InstallPackage	Action that installs the incoming package.
Instance	Stores values from weka.core.Instance objects, with X being the attribute index (integer) and Y being the internal value (double).
InstanceComparator	For comparing instance objects.
InstanceCompare	Stand-alone version of the Instance Compare utility.
InstanceCompare	For comparing two datasets visually.
InstanceCompareDefinition	Definition for the InstanceCompare props file.
InstanceComparePanel	A tool for comparing two datasets visually.
InstanceComparePanel.DatasetIndexer	Helper class for indexing the rows of a dataset.
InstanceComparePanel.DatasetPanel	Specialized panel for loading dataset and setting various parameters.
InstanceContainer	A container class for a weka.core.Instance wrapped in a weka.core.Instance.
InstanceContainerDisplayIDGenerator	Class for generating display IDs for Instance objects (based on weka.core.Instance objects).
InstanceContainerList	A panel that lists Instances in a JTable.
InstanceContainerManager	A handler for the Instance containers.
InstanceContainerModel	A model for displaying the currently loaded Instance objects.
InstanceContainerTableColumnNameGenerator	Abstract class for generating the column names of a table.
InstanceDumperVariable	Generates a sub-flow that sets a variable for the adams.flow.transformer.WekaInstanceDumper transformer's outputPrefix property using a prefix based on the full flow name.
InstanceExplorer	For displaying and filtering instances.
InstanceExplorer	A panel for exploring Instances visually.
InstanceExplorerDefinition	Definition for the InstanceExplorer props file.
InstanceExplorerHandler	Displays the following WEKA dataset types in the Instance Explorer: csv,arff,arff.gz,xrff,xrff.gz Valid options are:
InstanceGeneratorWithAdditionalFields	Generators with additional fields.
InstanceGeneratorWithFields	Generators with fields.
InstanceGrouping	Groups rows in a dataset using a regular expression on a nominal or string attribute.
InstanceLinePaintlet	Paintlet for generating a line plot for Instance objects.
InstanceLinePaintlet.MarkerShape	Enum for the marker shape to plot around the data points.
InstancePanel	A panel for displaying instances.
InstancePoint	A 2-dimensional point (X: attribute index, Y: internal value).
InstancePointComparator	A comparator for InstancePoint objects.
InstancePointHitDetector	Detects selections of instance points in the instance panel.
InstanceReader	Reads WEKA datasets in various formats.
InstanceReportFactory	A factory for GUI components for Instance-related reports.
InstanceReportFactory.Panel	A specialized panel that displays reports.
InstanceReportFactory.Table	A specialized table for displaying a Report.
InstancesColumnComboBox	ComboBox that lists the attribute names of the associated Instances in alphabetical order and when the user selects one, ensures that this column is visible.
InstancesColumnComboBox.ColumnContainer	Container for storing column name and
InstancesCrossValidationFoldGenerator	Split generator that generates folds for cross-validation for Instances objects.
InstancesGroupedCrossValidationFoldGenerator	Split generator that generates folds for cross-validation for Instances objects.
InstancesGroupedRandomSplitGenerator	Random split generator that works on Instances objects (groups instances).
InstancesHeaderRow	Header row for an Instances object.
InstancesIndexedSplitsRunsCompatibility	Performs compatibility tests between indexed splits configurations and Weka Instances objects.
InstancesIndexedSplitsRunsEvaluation	Evaluates the specified classifier on the indexed splits runs applied to the incoming data.
InstancesIndexedSplitsRunsGenerator	Indicator interface for generators that process Instances objects.
InstancesIndexedSplitsRunsPredictions	Trains the referenced classifier on the training splits and generates predictions for the test splits.
InstancesPanel	Panel displaying an Instances table.
InstancesPlot	Displays plot of Instances.
InstancesRandomSplitGenerator	Random split generator that works on Instances objects.
InstancesSortDefinitionPanel	Represents a single sorting definition.
InstancesSortPanel	Panel that allows users to sort instances over an arbitrary number of columns.
InstancesSortSetupEvent	Event that gets sent when the InstancesSortPanel setup changes.
InstancesSortSetupEvent.EventType	The type of event.
InstancesSortSetupListener	Interface for listeners that react to changes in a sort setup of a InstancesSortPanel.
InstancesSummaryPanel	This panel just displays relation name, number of instances, and number of attributes.
InstancesTable	Table for displaying Instances objects.
InstancesTableModel	The model for the Instances.
InstancesTablePopupMenuItem	Ancestor for menu items of popups for the InstancesTable.
InstancesTablePopupMenuItemHelper	Helper class for constructing popup menus for the InstancesTable.
InstancesTablePopupMenuItemHelper.TableState	Container object for the table state used by the popup menu items.
InstancesView	Provides a view of an Instances object.
InstancesView	Presents a view of an Instances object.
InstancesViewCreator	Interface for classes that generate Weka Instances views.
InstancesViewSupporter	Interface for classes that support Weka Instances views.
InstanceTab	Visualizes the selected dataset like the instance explorer.
InstanceTable	A specialized table for displaying an Instances object.
InstanceTableModel	A generic table model for displaying weka.core.Instances objects.
InstanceUtils	Utility class for instances.
InstanceView	Wrapper around an Instance object.
InstanceZoomOverviewPaintlet	Highlights the current zoom in the instance panel.
InstanceZoomOverviewPanel	Panel that shows the zoom in the TIC panel as overlay.
InterquartileRangeSamp	A sampling filter for detecting outliers and extreme values based on interquartile ranges.
InterquartileRangeSamp.IQRs	Container class for the IQR values.
InterQuartileRangeViewer	Displays internal values of the InterquartileRange filter.
IntervalEstimatorBased	Uses a classifier that produces confidence intervals.
IntervalEstimatorBased.SortedInterval	Helper class for sorting the confidence intervals.
Invert	Inverts the selected columns of the provided sub-column-filter.
Invert	Inverts the selected rows of the provided sub-row-filter.
InvertInstancesColumnFinder	Encloses a Instances ColumnFinder in Invert.
InvertInstancesRowFinder	Encloses a Instances RowFinder in Invert.
InvestigatorAsNewDataset	Allows the user to add the selected rows as new dataset in the Investigator.
InvestigatorJob	For running jobs in the InvestigatorPanel.
InvestigatorManagerPanel	Manages multiple sessions of the Investigator.
InvestigatorPanel	The main panel for the Investigator.
InvestigatorTabbedPane	Tabbed pane for managing the tabs of the Investigator.
InvestigatorTabJob	For running jobs in a AbstractInvestigatorTab.
InvestigatorTabRunnableJob	For executing Runnable's in a AbstractInvestigatorTab.
InvestigatorWorkspaceHelper	Helper class for Weka Investigator workspaces.
InvestigatorWorkspaceList	Lists the sessions.
JdbcOutputPanel	Stores the results in a JDBC database.
JFreeChart	Allows to perform a simple plot of a column or row.
JoinAttributes	A simple filter that joins several attributes into a single STRING one, with a user defined string acting as 'glue'.
JoinOnID	Joins the datasets by concatenating rows that share a unique ID.
JsonAdamsExperimentReader	Reads ADAMS Experiments in JSON format.
JsonAdamsExperimentWriter	Writes ADAMS experiments in JSON format.
JSONSpreadSheetReader	Reads WEKA datasets in JSON format and turns them into spreadsheets.
JSONSpreadSheetWriter	Writes a spreadsheet in JSON file format.
KeepRange	Keeps only the range of rows, in the order specified.
KennardStone	Applies the Kennard-Stone algorithm to the dataset. Each row has the pre-filter (eg PLS) applied before performing the search.
KernelPLS
LastAttribute	Simply chooses the last attribute as class attribute.
LatestRecords	Retains the latest database records.
LeanMultiScheme	Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
LeastMedianSq	Finds the base classifier with the best least median squared error.
LeaveOneOutByValueGenerator	Generates train/test split pairs using the unique values from the specified attribute.
LegacyClassifierErrors	Generates classifier errors plot (legacy Weka output).
LegacyCostBenefitAnalysis	Generates cost benefit analysis (legacy Weka output).
LegacyCostCurve	Generates cost curve (legacy Weka output).
LegacyGraphVisualizer	Displays the graph that the model generated (legacy Weka output).
LegacyMarginCurve	Generates margin curve (legacy Weka output).
LegacyThresholdCurve	Generates margin curve (legacy Weka output).
LegacyTreeVisualizer	Displays the tree that the model generated (legacy Weka output).
LegacyTreeVisualizer	Displays the tree that the model generated (legacy Weka output).
LibSVMSpreadSheetReader	Reads WEKA datasets in LibSVM format and turns them into spreadsheets.
LibSVMSpreadSheetWriter	Writes a spreadsheet in LibSVM file format.
LinearRegressionAttributeEval	Uses the coefficients of LinearRegressionJ to determine the importance of the attributes (attribute selection turned off, no elimination of collinear attributes).
LinearRegressionJ	Class for using linear regression for prediction.
ListPackages	Lists the packages.
ListPackages.ListType	The type of list to generate.
ListPackages.OutputFormat	How to output the packages.
LoadDatasetDialog	A dialog for loading datasets from disk.
LogClassRegressor	Takes log of the class attribute in the data.
LogPanel	The log panel.
LogTab	Just displays the log messages.
LogTargetRegressor	Takes logs of all numeric attributes in the data.
LogTransform	Transforms all numeric attributes in the specified range using a log-transform. The class attribute is omitted. If a value less or equal to zero is encountered, a missing value is output.
LowerStatistic	Enumeration of lower bound statistics to compute.
LWLDatasetBuilder	Class for building LWL-style weighted datasets.
LWLDatasetBuilder.LWLContainer	the container with the weighted dataset, distances, indices.
LWLIntervalEstimator	Locally weighted learning.
LWLSynchro	Locally weighted learning.
LWLSynchroPrefilter	Locally weighted learning.
M5Base2	M5Base.
M5P2	M5Base.
MakeCompatibleDatasets	For making compatible ARFF datasets.
MapToWekaInstance	Converts a map into a Weka Instance, using the provided storage object (Instances) as template.
MarginCurve	Displays a margin curve.
MatchWekaInstanceAgainstFileHeader	Matches an Instance against a dataset header loaded from a file, i.e., it automatically converts STRING attributes into NOMINAL ones and vice versa. The file can be any format that WEKA recognizes.
MatchWekaInstanceAgainstStorageHeader	Matches an Instance against a dataset header from storage, i.e., it automatically converts STRING attributes into NOMINAL ones and vice versa.
MathExpressionClassifier	Simple classifier that uses a pre-defined formula that can make use of attribute values using their names. Grammar: expr_list ::= '=' expr_list expr_part | expr_part ; expr_part ::= expr ; expr ::= ( expr ) # data types | number | string | boolean | date # constants | true | false | pi | e | now() | today() # negating numeric value | -expr # comparisons | expr < expr | expr <= expr | expr > expr | expr >= expr | expr = expr | expr != expr (or: expr <> expr) # boolean operations | ! expr (or: not expr) | expr & expr (or: expr and expr) | expr | expr (or: expr or expr) | if[else] ( expr , expr (if true) , expr (if false) ) | ifmissing ( variable , expr (default value if variable is missing) ) | isNaN ( expr ) # arithmetics | expr + expr | expr - expr | expr * expr | expr / expr | expr ^ expr (power of) | expr % expr (modulo) ; # numeric functions | abs ( expr ) | sqrt ( expr ) | cbrt ( expr ) | log ( expr ) | log10 ( expr ) | exp ( expr ) | sin ( expr ) | sinh ( expr ) | cos ( expr ) | cosh ( expr ) | tan ( expr ) | tanh ( expr ) | atan ( expr ) | atan2 ( exprY , exprX ) | hypot ( exprX , exprY ) | signum ( expr ) | rint ( expr ) | floor ( expr ) | pow[er] ( expr , expr ) | ceil ( expr ) | min ( expr1 , expr2 ) | max ( expr1 , expr2 ) | year ( expr ) | month ( expr ) | day ( expr ) | hour ( expr ) | minute ( expr ) | second ( expr ) | weekday ( expr ) | weeknum ( expr ) # string functions | substr ( expr , start [, end] ) | left ( expr , len ) | mid ( expr , start , len ) | right ( expr , len ) | rept ( expr , count ) | concatenate ( expr1 , expr2 [, expr3-5] ) | lower[case] ( expr ) | upper[case] ( expr ) | trim ( expr ) | matches ( expr , regexp ) | trim ( expr ) | len[gth] ( str ) | find ( search , expr [, pos] ) | replace ( str , pos , len , newstr ) | substitute ( str , find , replace [, occurrences] ) ; Notes: - Variables are either all upper case letters (e.g., "ABC") or any character apart from "]" enclosed by "[" and "]" (e.g., "[Hello World]"). - 'start' and 'end' for function 'substr' are indices that start at 1. - Index 'end' for function 'substr' is excluded (like Java's 'String.substring(int,int)' method) - Line comments start with '#'. - Semi-colons (';') or commas (',') can be used as separator in the formulas, e.g., 'pow(2,2)' is equivalent to 'pow(2;2)' - dates have to be of format 'yyyy-MM-dd' or 'yyyy-MM-dd HH:mm:ss' - times have to be of format 'HH:mm:ss' or 'yyyy-MM-dd HH:mm:ss' - the characters in square brackets in function names are optional: e.g.
MatlabSpreadSheetReader	Reads WEKA datasets in ARFF format and turns them into spreadsheets.
MatlabSpreadSheetWriter	Writes a spreadsheet in ARFF file format.
MatrixHelper	Helper class for the matrix-algorithm library.
MatrixHelper	Some matrix operations.
MatrixTab	Visualizes the selected dataset as matrix plot.
Mean	Performs the correction using simply the mean.
Measure	The measure to use for evaluating.
MemoryContainer	Dataset exists only in memory.
MenuItemComparator	Comparator for sorting the menu items for the history panel.
MenuItemComparator	Comparator for sorting the menu items for the per-fold pane.
Merge	Merges the selected datasets (side-by-side).
MergeDatasets	For merging datasets (side-by-side) into single dataset.
MergeManyAttributes	Merges two or more attributes, offers various strategies if values differ or not present. Uses the common subsequence (either from start or end) of the attributes as name of the merged attribute, otherwise the concatenation of them (separated by '-').
MergeTwoAttributes	Merges two attributes, offers various strategies if values differ or not present. Uses the common subsequence (either from start or end) of the two attributes as name of the merged attribute, otherwise the concatenation of the both (separated by '-').
MetaPartitionedMultiFilter	With each specified filter, a regular expression is associated that defines the range of attributes to apply the filter to.
MinMaxLimits	Allows to influence the handling of lower/upper limits of the built classifier when making predictions. The following types of handling are available: AS_IS, MANUAL, CLASS_RANGE Details on the types: - AS_IS: prediction does not get changed - MANUAL: applies the manual limit, ie at most this limit is output - CLASS_RANGE: applies the percentage leeway to the class attribute range of the training set to determine the actual limit value.
MinMaxLimits.LimitHandling	Determines the type of handling for the limit
ModelOutput	Outputs the model if available.
ModelOutput	Outputs the model if available.
ModelOutput	Outputs the model if available.
ModelOutputHandler	Interface for classes that allow user to decide whether to output a model in string representation or not.
MonitoringDataContainer	Interface for data containers that monitor their source for changes.
MSLE	Computes the mean squared log error (MSLE) for regression models.
MultiAttributeSummaryPanel	Can display one or more instances of AttributeSummaryPanel class.
MultiAttributeVisualizationPanel	Can display one or more instances of AttributeVisualizationPanel class.
MultiClassifiersCombinerModels	Generates a MultipleClassifiersCombiner meta-classifier from the incoming pre-built classifier models.
MultiCleaner	Combines multiple cleaners, applies them sequentially.
MultiClustererPostProcessor	Applies the specified post-processors sequentially.
MultiColumnFinder	Applies multiple column finding algorithms to the data. The indices can be either joined or intersected.
MultiColumnFinder.Combination	How combine the indices.
MultiExperimenter	Extended interface for the WEKA Experimenter, allowing for an arbitrary number of Experimenter panels.
MultiExplorer	Opens the (multi-version of the) WEKA Explorer.
MultiExplorer	Extended interface for the WEKA Explorer, allowing for an arbitrary number of Explorer panels.
MultiLevelSplitGenerator	Generates splits based on groups extracted via regular expressions.
MultiplicativeScatterCorrection	Performs Multiplicative Scatter Correction, using the specified correction scheme.
MultiPLS	For each Y that gets identified by the regular expression for Y attributes, the specified PLS (partial least squares) algorithm gets applied to the X attributes identified by the corresponding regular expression.
MultiPostProcessor	Applies the specified post-processors sequentially to the input data and combines their output.
MultiRowFinder	Applies multiple row finding algorithms to the data. The indices can be either joined or intersected.
MultiRowFinder.Combination	How combine the indices.
MultiRowProcessor	Uses the specified row selection scheme to identify groups of rows in the data coming through and then applies the selected row processor to these subsets.
MultiTokenizer	Combines the tokens of several tokenizers, skipping duplicate tokens.
NestedAdamsExperimentReader	Reads ADAMS Experiments in nested format.
NestedAdamsExperimentWriter	Writes ADAMS experiments in nested format.
NewNNSearch	Class implementing the brute force search algorithm for nearest neighbour search.
NIPALS
NoChange	Simply returns the current relation name.
NoClassAttribute	Never returns a class attribute.
NominalToNumeric	Converts a nominal attribute into a numeric one.
NominalToNumeric.ConversionType	Enumeration of conversion types.
NormalizeAdaptive	Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
NormalizeDuplicateChars	Replaces all duplicate characters with a single one.
Null	Does not generate a final model.
NullCommunicationProcessor	Dummy, does nothing.
NullFinder	Dummy finder, does not find any columns.
NullFinder	Dummy finder, does not find any rows.
NumericErrorScalerWithReference	Scales the errors for numeric class attributes, using an user-specified error as reference point for a specified size.
OPLS
OuterProductAnalysis	Performs Outer Product Analysis (OPA). For more information, see: Fabricio S.Terra, Raphael A.Viscarra Rossel, Jose A.M.Dematte (2019).
OutputPanel	Allows the user to select the output type, e.g., ARFF file or JDBC database.
OutputPrefixType	Defines what kind of prefix to use for outputting data and setups.
OutputTabbedPane	Tabbed pane for the output.
OutputType	Defines what to output during a genetic algorithm run.
PAA	Valid options are:
PackageManager	Opens the WEKA PackageManager.
PackData	???
PackDataDef	???
PackDataDef.DataInfo
PackDataGeneticAlgorithm	???
PackDataGeneticAlgorithm	???
PackDataInitialSetupsProvider<T extends PackDataGeneticAlgorithm>
PartialLeastSquaresTab	Visualizes the PLS loadings and PLS space calculated from the selected dataset.
PartitionedMultiFilter2	A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
PartitionedStacking	Builds the base-classifiers on subsets of the data defined by ranges that correspond to the base-classifiers.
PassThrough	A dummy evaluator that OKs all data.
PassThrough	Simply returns the same setups.
PassThrough	Dummy post-processor that just returns the model container as it is.
PassThrough	Does nothing, just passes through the input data.
PassThrough	Dummy, never removes a token.
PassThrough	Just passes through the data.
PCA	Performs principal components analysis and allows access to loadings and scores.
PCANNSearch	Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
PeakTransformed	Uses the maximum peak in the instances.
PerFoldMultiPagePane	Specialized multi-page pane for managing per-fold data.
PlainTextResultsPanel	Displays the results in plain text.
PlotAttributeVsAttribute	Allows the user to select a dataset and plot attribute vs attribute (selected by user).
PlotColumn	Interface for plugins that plot a column.
PlotRow	Interface for plugins that plot a row.
PlotSelectedRows	Interface for plugins that plot selected rows.
PLS	Performs partial least squares analysis and allows access to loadings and scores.
PLS	Applies the specified partial least squares (PLS) algorithm to the data.
PLS1	Implementation of PLS1 algorithm. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
PLS1AttributeEval	Uses the first component of PLS1 to determine the importance of the attributes (defaults: no preprocessing, missing values not replaced, and 20 components)
PLSClassifierWeighted	A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
PLSClassifierWeightedWithLoadings	A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions. Allows access to the PLS matrices in case the filter is a PLSFilterWithLoadings instance.
PLSFilterExtended	Class contains changes to the Weka's PLSFilter in order to have simpls work with multiple y attributes.
PLSFilterNumComponents	SavitzkyGolay numPoints handler.
PLSFilterWithLoadings	Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. Allows access to the internal matrices. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
PLSMatrixAccess	For classes that allow access to PLS matrices.
PLSNNSearch	Class implementing the brute force search algorithm for nearest neighbour search, filtered using PLS.
PLSWeighted	A wrapper classifier for the PLS filter, utilizing the filter's ability to perform predictions.
PreCleanedTokenizer	Allows the cleaning of tokens before actual tokenization.
PredictionEccentricity	Generates classifier prediction eccentricity.
PredictionHelper	Helper class for dealing with predictions from result items.
Predictions	Generates statistics for predictions from repeated cross-validation runs.
Predictions	Displays the predictions.
Predictions	Generates statistics for predictions from repeated cross-validation runs.
PredictionTrend	Generates a 'prediction trend' for classifier errors: sorts the predictions on the actual value and plots actual and predicted side-by-side.
PredictionType	The type of prediction to perform.
PredictionUtils	Helper class for predictions from repeated cross-validation runs.
PreprocessHandler	Manages the PreprocessPanel.
PreprocessingType	The preprocessing type.
PreprocessTab	Preprocessing tab.
PrincipalComponentsJ	* Performs a principal components analysis and transformation of the data. * Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%). * Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
PrincipalComponentsTab	Visualizes the PCA loadings and PCA space calculated from the selected dataset.
PRM
ProcessCell	Interface for plugins that process a cell.
ProcessColumn	Interface for plugins that process a column.
ProcessRow	Interface for plugins that process a row.
ProcessSelectedRows	Interface for plugins that processes selected rows.
PublicPrincipalComponents	Class that is identical to the Principal components class except it contains a public method to get the coefficients from the principal components model
PullUpClassifier	For pulling up classifiers from SingleClassifierEnhancer wrappers.
PullUpClusterer	For pulling up clusterers from SingleClustererEnhancer wrappers.
PullUpInstancesColumnFinder	Pulls up the base Instances ColumnFinder from a filtered ColumnFinder.
PullUpInstancesRowFinder	Pulls up the base Instances RowFinder from a filtered RowFinder.
PyroProxy	Proxy for a python model using Pyro4 for communication.
PyroProxyObject	Interface for classes that make use of Pyro4.
Randomize	Randomizes the selected dataset.
RandomModelTrees
RandomRegressionForest	RandomRegressionForest: subtract mean and pls, then grow completely random trees (leaf: min ..
RandomSplitGenerator	Interface for generators of random splits of datasets.
RandomSubset	Creates a random subset from a dataset and inserts it as a new dataset.
RangeBased	Performs the correction using slopes/intercepts calculated for the defined ranges.
RangeBased	Performs the correction using slopes/intercepts calculated for the defined ranges.
RangeCheck	Keeps track of the ranges in case of numeric attributes.
RangeCheckClassifier	Interface for classifiers that allow checks whether data is outside the training range of the classifier.
RangeCheckHelper	Helper class for generating range checks.
ReducedData	Generates the reduced dataset.
ReevaluateModel	Re-evaluates a serialized model.
ReevaluateModel	Re-evaluates a serialized model.
RefreshCache	Refreshes the package cache.
RelativeNumericErrorScaler	Scales the errors for numeric class attributes.
RemoteWekaExperimentIO	IO handler for remote experiments.
RemoteWekaExperimentRunner	A class that handles running a copy of the experiment in a separate thread.
Remove	Removes the selected attributes.
RemoveDuplicateIDs	Removes rows with IDs that occur multiple times. Also skips rows with missing ID.
RemoveDuplicates	Removes all duplicate instances.
RemoveInstancesWithMissingValue	Removes all instances that contain missing values.
RemoveMisclassifiedAbs	A filter that removes instances which are incorrectly classified.
RemoveMisclassifiedRel	A filter that removes instances which are incorrectly classified.
RemoveNonWordCharTokens	Removes tokens that contain non-word characters.
RemoveOutliers	Cross-validates the specified classifier on the incoming data and applies the outlier detector to the actual vs predicted data to remove the outliers. NB: only works on full dataset, not instance by instance.
RemoveTestInstances	Removes all instances of the provided test set from the data passing through. Requires an attribute in the data that uniquely identifies instances across datasets.
RemoveTestSet	Removes the test instances from one dataset in another.
RemoveWithLabels	Allows the user to remove nominal labels via a regular expression.
RemoveWithWeights	Removes instances with weights outside the defined limits.
RemoveWithZeroes	Removes all instances that contain at least the specified number (or percentage) of zeroes in numeric attributes.
RemoveWorst	Removes the worst predictions, which are considered outliers that detract from the actual model performance.
RemoveWorstStdDev	Removes the worst predictions, which are considered outliers that detract from the actual model performance.
Rename	Renames the selected dataset.
Rename	Renames the selected attribute.
ReorderAttributes	Allows the user to reorder the attributes.
RepeatedCrossValidation	Performs repeated cross-validation.
ReplaceMissingValuesWithZero	Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
ReportColorInstancePaintlet	Paintlet for generating a line plot using the color stored in the report.
ReportToWekaInstance	Converts a report into a weka.core.Instance objects.
ResettableExperiment	Interface for experiments that can clear any prior results.
ResidualsVsFitted	Plots the residuals vs the fitted values (= predictions).
ResidualsVsPredictor	Plots the residuals vs the predictor.
ResultItem	Container for an evaluation, model, training set header.
ResultItem	Container for an attribute selection, evaluator and search method.
ResultItem	Container for an evaluation, model, training set header.
ResultItem	Container for an evaluation, model, training set header.
ResultItem	Container for an experiment run.
ResultMatrixAdamsCSV	Generates the matrix in ADAMS CSV ('comma-separated values') format.
ResultMatrixMediaWiki	Generates table output in MediaWiki format.
Revert	Reverts the selected dataset (if possible).
ROC	Displays ROC curve data.
RoundErrorScaler	Performs no scaling at all, just rounds the error to the next integer.
RowFilteredColumnFinder	This column finder first filters the rows before finding any columns on the subset of rows.
RowFinder	Interface for classes that "find" rows of interest in datasets.
RowNorm	Row wise normalization.
RowSplitter	Splits a dataset in two based on the rows selected by the row-finder.
RowStatistic	Allows the calculation of row statistics.
RowSum	Sums up all numeric values in a row and replaces them with it.
RPD	Computes the RPD (Ratio of Performance to Deviation) for regression models: RPD = SD / RMSE https://www.academia.edu/4303409/Why_you_dont_need_to_use_RPD
RSquared	Computes the R^2 for regression models.
Rule2	Generates a single m5 tree or rule
RuleNode2	Constructs a node for use in an m5 tree or rule
Rules	Outputs the rules if available AssociationRulesProducer.
RunInformation	Generates run information.
RunInformation	Generates run information.
RunInformation	Generates run information.
RunInformation	Generates run information.
RunInformation	Generates run information.
RunInformationHelper	Helper class for run information.
SafeRemoveRange	A filter that removes a given range of instances of a dataset. Works just like weka.filters.unsupervised.instance.RemoveRange, but has a more robust handling of instance ranges.
SamplePlot	Generates plot containers with statistics derived for each sample across the cross-validation runs.
SamplePlot	Generates a plot with statistics derived for each sample across the cross-validation runs.
Save	Saves the selected data.
SaveAs	Allows the saving of an instance container.
SaveGraph	Allows user to save graph (eg generated by BayesNet) as file.
SaveIndexedSplitsRuns	Saves the indexed splits runs generated from the selected data.
SaveTree	Saves a tree in dotty notation as file.
SaveVisible	Allows the user to save the visible containers as ARFF.
SavitzkyGolay	A filter that applies Savitzky-Golay smoothing. If a class attribute is present this will not be touched and moved to the end. For more information see: A.
SavitzkyGolay2	A filter that applies Savitzky-Golay smoothing. If a class attribute is present this will not be touched and moved to the end. For more information see: A.
SavitzkyGolay2NumPoints	SavitzkyGolay numPoints handler.
SAX	A simple filter that retains only every nth attribute.
SAXDistance	Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
SAXKMeans	SimpleKMeans adapted for SAX.
Scale	Scales all numeric attributes between the specified min/max.
ScatterPlotTab	For plotting attributes against each other.
SDR	Computes the SDR (Standard Deviation of Residuals) for regression models.
SerializedAdamsExperimentReader	Reads serialized ADAMS Experiments.
SerializedAdamsExperimentWriter	Writes serialized ADAMS experiments.
SerializedFilter	Processes the data with a the (trained) filter deserialized from the specified file.
SetMissingValue	Attribute values in the given range are set to missing values. NB: The class attribute is not excluded from this process.
Simple	Just merges the datasets side by side.
Simple	Simply builds the classifier on the full dataset.
Simple	Simple preparation scheme, using JSON with the actual data in CSV format.
Simple.SimpleRowSetIterator	Enumeration class which just returns the concatenation of the source data rows in order.
SimpleArffLoader	A simple ARFF loader, only supports batch loading.
SimpleArffSaver	Writes the Instances to an ARFF file in batch mode.
SimpleDetrend	Performs Detrend, using the specified correction scheme.
SimpleInstanceLinePaintlet	Paintlet for generating a line plot for Instance objects (no markers).
SimpleInstancePanelUpdater	Updates the flow after the specified number of tokens have been processed.
SimpleJsonCommunicationProcessor	Turns Instances/Instance into simple JSON.
SimpleLinearRegressionIntervalEstimator	Learns a simple linear regression model.
SimpleLinearRegressionWithAccess	Learns a simple linear regression model.
SimplePlot	Allows to perform a simple plot of a column or row.
SimpleSubRange	Generates an Evaluation object based on the actual class values that fall within the specified interval ranges.
SIMPLS	Implementation of SIMPLS algorithm. Available matrices: W, B For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
SIMPLSAttributeEval	Uses the first component of SIMPLS to determine the importance of the attributes (defaults: no preprocessing, missing values not replaced, and 20 components)
SIMPLSMatrixFilter	Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. Allows access to the internal matrices. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
SIMPLSMatrixFilterFromGeneticString	Runs Partial Least Square Regression over the given instances and computes the resulting beta matrix for prediction. By default it replaces missing values and centers the data. Allows access to the internal matrices. For more information see: Tormod Naes, Tomas Isaksson, Tom Fearn, Tony Davies (2002).
SIMPLSWeightsMatrix	SIMPLS pls internal weights handler.
SocketFacade	Uses sockets to communicate with a process for training and making predictions.
Sort	Sorts the instances.
SortOnAttribute	Sorts the instances on a particular attribute.
SourceCode	Outputs source code from the model (if classifier implements Sourcable).
SparsePLS
SpellChecker	A simple filter that merges misspelled labels into a single correct one.
Split	Creates train/test splits from a dataset and inserts these as new datasets.
SplitGenerator	Interface for helper classes that generate dataset splits.
SpreadSheet	For loading ADAMS spreadsheets.
SpreadSheetContainer	SpreadSheet-based dataset.
SpreadSheetLoader	Loads a CSV file using an ADAMS spreadsheet reader and converts it into an Instances object.
SpreadSheetSaver	Writes the Instances to a spreadsheet file using the specified ADAMS spreadsheet writer.
SpreadSheetToWekaInstances	Generates a weka.core.Instances object from a SpreadSheet object. If there are too many unique lables for a NOMINAL attribute, it gets turned into a STRING attribute (see 'maxLabels' property).
SqlPanel	A simple demonstration for extending the Explorer by another tab, in this case the SqlViewer (as an extra tab instead of only the button in the PreprocessPanel).
SqlViewer	Opens the SQL viewer.
Statistics	Generates mean/stddev for the specified statistics.
Statistics	Generates statistics for repeated cross-validation runs.
StoppableClassifier	Ancestor for classifiers that can be stopped.
StoppableEvaluation	Extended Evaluation class that can stop its evaluation processes better.
StoppableEvaluation	Extended Evaluation class that can stop its evaluation processes better.
StoppableRandomizableClassifier	Ancestor for randomizable classifiers that can be stopped.
StoppableSingleClassifierEnhancer	Stops the base classifier if it implements Stoppable.
StringToDate	Parses the selected range of string attributes using the specified format and turns them into date ones.
SubRange	Generates an Evaluation object based on the actual class values that fall within the specified interval ranges.
SubRangeEvaluation	Generates a fake evaluation using only predictions with an actual class value that fits in the specified sub-range.
SubRangeEvaluation	Generates a fake evaluation using only predictions with an actual class value that fits in the specified sub-range.
SubsetEnsemble	Generates an ensemble using the following approach: - for each attribute apart from class attribute do: * create new dataset with only this feature and the class attribute * remove all instances that contain a missing value * if no instances left in subset, don't build a classifier for this feature * if at least 1 instance is left in subset, build base classifier with it If no classifier gets built at all, use ZeroR as backup model, built on the full dataset. In addition to the default feature for a subset, a number of random features can be added to the subset before the classifier is trained. At prediction time, the Vote meta-classifier (using the pre-built classifiers) is used to determing the class probabilities or regression value.
SumTransformed	Finds the base classifier with the best least median squared error.
Supplementary	Outputs the supplementary data if available.
SuppressModelOutput	Meta-classifier that enables the user to suppress the model output. Useful for ensembles, since their output can be extremely long.
SVMLightSpreadSheetReader	Reads WEKA datasets in ARFF format and turns them into spreadsheets.
SVMLightSpreadSheetWriter	Writes a spreadsheet in SVMLight file format.
SwapPLS	Swaps one PLS filter for another.
TableContentPanel	Panel for exporting the table as spreadsheet.
TableResultsPanel	Displays the results in a table.
TestingHelper	Helper class for evaluating models on test data.
TestingHelper.TestingUpdateListener	The interface for objects that listen for testing updates.
TextDirectory	Uses the TextDirectoryLoader to load text documents.
TextDirectoryLoaderContainer	Dataset generated by TextDirectoryLoader.
TextOutput	Generates textual output.
TextStatistics	Generates basic text statistic.
TextStatistics	Generates basic text statistic.
TextStatistics	Generates basic text statistic.
TextualContentPanel	Panel for exporting the textual component as text.
ThreadSafeClassifier	Indicator interface for thread-safe classifiers.
ThreadSafeClassifierWrapper	Wraps an abstaining classifier and allows turning on/of abstaining.
ThresholdCurves	Displays all the threshold curves (ROC) in a single plot.
ThresholdedBinaryClassification	Meta classifier for binary classification problems that allows to specify a minimum probability threshold for one of the labels.
TokenCleaner	Interface for token cleaners.
Train	Builds an associator on a dataset.
Train	Performs attribute selection on the train data.
TrainableColumnFinder	Interface for ColumnFinder algorithms that can be trained.
TrainableRowFinder	Interface for RowFinder algorithms that can be trained.
TrainTestSet	Uses dedicated train/test sets.
TrainTestSet	Uses dedicated train/test sets.
TrainTestSplit	Uses a (random) percentage split to generate train/test.
TrainTestSplit	Uses a (random) percentage split to generate train/test.
TrainTestSplitExperiment	Performs train-test splits.
TrainTestSplitExperiment.TrainTestSplitExperimentJob
TrainTestSplitSetup	Setup for a train/test-split experiment.
TrainValidateTestSet	Uses dedicated train/validate/test sets.
TransformNNSearch
TreeGraphML	Displays the GraphML source code of the tree graph.
TreeVisualizer	Displays data in the tree visualizer.
TreeVisualizer	Displays trees in dot notation.
TreeVisualizer	Displays the tree that the model generated.
Uninstall	Action that removes installed packages.
UpperStatistic	Enumeration of upper statistics to compute.
UseAsClass	Uses the selected attribute as class attribute.
VCPLS
Veto	If the specified label is predicted by the required minimum number of classifiers of the ensemble, then this label is predicted.
ViewAsTable	Views the selected instance as table.
ViewCell	For viewing the cell content.
Viewport	Allows the user to perform operations on the instances visible in the current viewport.
VotedFolds	Generates a Vote meta-classifier from the models from the cross-validation folds.
VotedImbalance	Generates an ensemble using the following approach: - do x times: * create new dataset, resampled with specified bias * build base classifier with it If no classifier gets built at all, use ZeroR as backup model, built on the full dataset. At prediction time, the Vote meta-classifier (using the pre-built classifiers) is used to determining the class probabilities or regression value. Instead of just using a fixed number of resampled models, you can also specify thresholds (= probability that the minority class does not meet) with associated number of resampled models to use.
VotedModels	Generates a Vote meta-classifier from the incoming pre-built classifier models.
VotedPairs	Generates an array of classifiers that contains the original ones, but also all possible classifier pairs encapsulated in the Vote meta-classifier.
VotedPairs.VotingType	How the voting is done.
WeightedEuclideanDistance	Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
WeightedEuclideanDistanceRidge	Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
WeightedInstancesHandlerWrapper	A meta-classifier that implements the weka.core.WeightedInstancesHandler interface in order to enable all classifiers to be used in other meta-classifiers that require the base classifier to implem ent the WeightedInstancesHandler interface.
WeightsBasedResample	Normalizes all instance weights and drops the ones that fall below the specified threshold, but at most the specified percentage. Of the left over instances, the smallest weight, e.g., 0.2, represents one instance, which translates a weight of 1.0 to five instances.
Weka	Generates features in spreadsheet format.
WekaAccumulatedError	Generates plot containers from an evaluation object's predictions.
WekaAccumulatedError.SortablePrediction	Container for a classifier prediction, used for sorting.
WekaAggregateEvaluations	Aggregates incoming weka.classifiers.Evaluation objects and forwards the current aggregated state.
WekaAssociatorContainer	Container for associators and their rules.
WekaAssociatorSetup	Outputs an instance of the specified associator.
WekaAttributeIndex	Extended Index class that can use an attribute name to determine an index of a attribute as well.
WekaAttributeIndexEditor	Editor for WekaAttributeIndex objects.
WekaAttributeIndexParsing	For parsing WekaAttributeIndex options.
WekaAttributeIterator	Iterates through all attributes of a dataset and outputs the names. The attributes can be limited with the range parameter and furthermore with the regular expression applied to the names. Instead of outputting the names, it is also possible to output the 1-based indices.
WekaAttributeRange	Extended Range class that also allows attribute names for specifying attribute positions (names are case-insensitive, just like placeholders for 'first', 'second', etc).
WekaAttributeRangeEditor	A PropertyEditor for WekaAttributeRange objects.
WekaAttributeRangeParsing	For parsing WekaAttributeRange options.
WekaAttributeSelection	Performs attribute selection on the incoming data. In case of input in form of a class adams.flow.container.WekaTrainTestSetContainer object, the train and test sets stored in the container are being used. NB: In case of cross-validation no reduced or transformed data can get generated! Input/output: - accepts: weka.core.Instances adams.flow.container.WekaTrainTestSetContainer - generates: adams.flow.container.WekaAttributeSelectionContainer Container information: - adams.flow.container.WekaTrainTestSetContainer: Train, Test, Seed, FoldNumber, FoldCount, Train original indices, Test original indices - adams.flow.container.WekaAttributeSelectionContainer: Train, Reduced, Transformed, Test, Test reduced, Test transformed, Evaluation, Statistics, Selected attributes, Seed, FoldCount
WekaAttributeSelectionContainer	A container for storing results from attribute selection.
WekaAttributeSelectionSummary	Outputs a summary string of the attribute selection.
WekaAttributeSummary	Displays an attribute summary.
WekaBootstrapping	Performs bootstrapping on the incoming evaluation and outputs a spreadsheet where each row represents the results from bootstrapping sub-sample.
WekaBootstrapping.ErrorCalculation	how to calculate the error.
WekaCapabilities	Filters weka.core.Instance and weka.core.Instances objects based on defined capabilities.
WekaCapabilitiesToInstances	Turns a weka.core.Capabilities object into a Weka dataset filled with random data that is compatible with these capabilities.
WekaCapabilitiesToSpreadSheet	Turns a weka.core.Capabilities object into a spreadsheet, listing all individual capabilities and whether they are supported.
WekaChooseAttributes	Lets the user select attributes interactively to use down the track. Internally, a weka.filters.unsupervised.attribute.Remove WEKA filter is constructed from the selection, to remove the attributes that the user didn't select.
WekaClassification	Uses the index of the classification, i.e., the predicted label, as index of the switch
WekaClassificationModel	Classification model for Weka classifiers.
WekaClassifier	Wraps around a Weka classifier that handles nominal classes (= classification).
WekaClassifierErrors	Actor for displaying classifier errors.
WekaClassifierErrors.DataGenerator	Helper class for generating visualization data.
WekaClassifierGenerator	Generates multiple classifier setups.
WekaClassifierInfo	Outputs information of a trained weka.classifiers.Classifier object.
WekaClassifierInfo.InfoType	The type of information to generate.
WekaClassifierModelLoader	Manages classifier models.
WekaClassifierOptimizer	Evaluates a classifier optimizer on an incoming dataset.
WekaClassifierRanker	Performs a quick evaluation using cross-validation on a single dataset (or evaluation on a separate test set if the number of folds is less than 2) to rank the classifiers received on the input and forwarding the x best ones.
WekaClassifierRanker.Measure	The performance measure to use.
WekaClassifierRanker.RankingJob	A job class specific to ranking classifiers.
WekaClassifierSetup	Outputs an instance of the specified classifier.
WekaClassifierSetupProcessor	Applies the specified processor to the incoming array of classifiers, e.g., for generating new or filtered setups.
WekaClassifying	Uses a serialized model to perform predictions on the data being passed through. The following order is used to obtain the model (when using AUTO): 1.
WekaClassManager	Uses WekaPackageClassLoaderManager.forName(...).
WekaClassSelector	Sets the class index.
WekaClusterAssignments	Outputs the cluster assignments from the evaluation.
WekaClusterer	Wraps around a Weka clusterer.
WekaClustererGenerator	Generates multiple clusterer setups.
WekaClustererInfo	Outputs information of a trained weka.clusterers.Clusterer object.
WekaClustererInfo.InfoType	The type of information to generate.
WekaClustererModelLoader	Manages clusterer models.
WekaClustererPostProcessor	Applies the specified post-processor to the cluster container (adams.flow.container.WekaModelContainer) See also: adams.flow.transformer.WekaTrainClusterer Input/output: - accepts: adams.flow.container.WekaModelContainer - generates: adams.flow.container.WekaModelContainer Container information: - adams.flow.container.WekaModelContainer: Model, Header, Dataset
WekaClustererSetup	Outputs an instance of the specified clusterer.
WekaClusterEvaluationContainer	A container for ClusterEvaluation objects, with optional trained model.
WekaClusterEvaluationSummary	Generates a summary string of the weka.clusterers.ClusterEvaluation objects that it receives.
WekaClustering	Uses a serialized model to cluster data being passed through. The following order is used to obtain the model (when using AUTO): 1.
WekaClusteringContainer	A container for clusterings made by a clusterer.
WekaClusteringModel	Clustering model for Weka classifiers.
WekaCommandLineHandler	Handles objects of classes that implement the weka.core.OptionHandler interface.
WekaCommandToCode	Applies a commandline converter to the incoming commandline to generate code. Uses the following project: https://github.com/fracpete/command-to-code-weka-package
WekaCommandToCode	For turning Weka commandline strings into code.
WekaConverter	Helper class for converting data to and fro Weka.
WekaCostBenefitAnalysis	Actor for displaying a cost benefit analysis dialog.
WekaCostCurve	Actor for displaying a cost curve.
WekaCrossValidationClustererEvaluator	Cross-validates a clusterer on an incoming dataset.
WekaCrossValidationEvaluator	Cross-validates a classifier on an incoming dataset.
WekaCrossValidationExecution	Performs cross-validation, either single or multi-threaded.
WekaCrossValidationJob	For evaluation of a single train/test fold in parallel.
WekaCrossValidationSplit	Generates train/test pairs like during a cross-validation run.
WekaDatabaseReader	Executes a query and returns the data either in batch or incremental mode.
WekaDatabaseWriter	Actor for saving a weka.core.Instances object in a database. The relation name of the incoming dataset can be used to replace the current filename (path and extension are kept).
WekaDataGenerator	Generates artificial data using a Weka data generator.
WekaDatasetHandler	Displays the following WEKA dataset types: csv,arff,arff.gz,xrff,xrff.gz Valid options are:
WekaDatasetsMerge	Merges 2 or more datasets into a single dataset, under a selectable merge method.
WekaDatasetSplit	Splits the incoming dataset into sub-sets using the specified splitter.
WekaDrawableToString	Extracts the string representation of a weka.core.Drawable object, e.g., the tree representation of a decision tree or the graph of a BayesNet.
WekaEditorsRegistration	Registers first the WEKA GenericObjectEditor editors and the ADAMS ones.
WekaEditorsRegistration.AccessibleGenericObjectEditor	Subclass of GenericObjectEditor to get access to the class hierarchies.
WekaEditorsRegistration.AccessiblePluginManager	For getting access to protected members in the package manager.
WekaEnsembleGenerator	Uses the specified generator to create ensembles from the incoming data.
WekaEvaluation	Provides further insight into an Evaluation object.
WekaEvaluationContainer	A container for Evaluation objects, with optional trained model.
WekaEvaluationInfo	Outputs information about a Weka weka.classifiers.Evaluation object.
WekaEvaluationInfo.InfoType	The type of information to output.
WekaEvaluationPostProcessor	Applies the specified post-processor to the incoming Evaluation data.
WekaEvaluationSummary	Generates a summary string of the weka.classifiers.Evaluation objects that it receives.
WekaEvaluationToCostCurve	Generates cost-curve data from a WEKA Evaluation object.
WekaEvaluationToMarginCurve	Generates margin-curve data from a WEKA Evaluation object.
WekaEvaluationToThresholdCurve	Generates threshold-curve data from a WEKA Evaluation object.
WekaEvaluationValuePicker	Picks a specific value from an evaluation object.
WekaEvaluationValues	Generates a spreadsheet from statistics of an Evaluation object.
WekaExperiment	Represents a Weka experiment, stored in a file.
WekaExperimentContainer	Container for Weka experiment results.
WekaExperimenterPreferencesPanel	Preferences for the WEKA Experimenter.
WekaExperimentEvaluation	Generates evaluation output of an experiment that was run previously.
WekaExperimentExecution	Executes an experiment.
WekaExperimentFile	A dummy class for the GOE, for special handling of experiments.
WekaExperimentFileEditor	A PropertyEditor for WekaExperimentFile objects that lets the user select a file.
WekaExperimentFileEditor.SimpleSetupDialog	A dialog for displaying the simple setup of an experiment.
WekaExperimentFileParsing	For parsing WekaExperimentFile options.
WekaExperimentFileReader	Loads an experiment file.
WekaExperimentFileWriter	Saves an experiment file.
WekaExperimentGenerator	Generates an experiment setup that can be used in conjunction with the Experiment transformer actor.
WekaExperimentGenerator.EvaluationType	The evaluation type.
WekaExperimentGenerator.ExperimentType	The experiment type.
WekaExperimentGenerator.ResultFormat	The data format the experiment data is stored in.
WekaExplorerPreferencesPanel	Preferences for the WEKA Explorer.
WekaExtractArray	Extracts a column or row of data from a weka.core.Instances or SpreadSheet object. Only numeric columns can be returned.
WekaExtractArray.ExtractionType	The type of extraction to perform.
WekaExtractPLSMatrix	Transformer that allows the extraction of internal PLS filter/classifier matrices, forwarding them as spreadsheets.
WekaExtractPLSMatrix.MatrixType	The type of PLS matrix to extract (either PLS1 or SIMPLS ones will be available).
WekaFileChooser	A specialized JFileChooser that lists all available file Readers and Writers for Weka file formats.
WekaFileReader	Reads any file format that Weka's converters can handle and returns the full dataset or single weka.core.Instance objects.
WekaFileReader.OutputType	Defines how to output the data.
WekaFileWriter	Actor for saving a weka.core.Instances object as file. The relation name of the incoming dataset can be used to replace the current filename (path and extension are kept).
WekaFilter	Applies a Weka filter to the data.
WekaFilter	Filters Instances/Instance objects using the specified filter. When re-using a trained filter, ensure that 'initializeOnce' is checked. The following order is used to obtain the model (when using AUTO): 1.
WekaFilter.BatchFilterJob
WekaFilterContainer	A container for filters and the filtered data.
WekaFilterGenerator	Generates multiple filter setups.
WekaFilterModelLoader	Model loader for Weka filters.
WekaGenericArrayEditorDialog	Displays a GenericArrayEditor.
WekaGenericArrayEditorPanel	A panel that contains text field with the current setup of the array and a button for bringing up the GenericArrayEditor.
WekaGenericObjectEditorDialog	Displays a GenericObjectEditor.
WekaGenericObjectEditorHandler	Handler for the WEKA GenericObjectEditor.
WekaGenericObjectEditorPanel	A panel that contains text field with the current setup of the object and a button for bringing up the GenericObjectEditor.
WekaGenericObjectEditorPopupMenu	Generic GOE popup menu, for copy/paste, etc.
WekaGenericPLSMatrixAccess	Transformer that allows the extraction of internal PLS filter/classifier matrices, forwarding them as spreadsheets. See the respective PLS implementation for details on available matrix names (derived from: weka.filters.supervised.attribute.pls.AbstractPLS) Input/output: - accepts: weka.classifiers.Classifier weka.filters.Filter weka.core.GenericPLSMatrixAccess adams.flow.container.WekaModelContainer - generates: adams.data.spreadsheet.SpreadSheet Container information: - adams.flow.container.WekaModelContainer: Model, Header, Dataset
WekaGeneticAlgorithm	Applies the genetic algorithm to the incoming dataset. Forwards the best setup(s) after the algorithm finishes. A callable sink can be specified for receiving intermediate performance results.
WekaGeneticAlgorithmContainer	A container for genetic algorithms output (setup, measure, fitness).
WekaGeneticAlgorithmInitializationContainer	A container for initializing genetic algorithms.
WekaGeneticAlgorithmInitializer	Populates a adams.flow.container.WekaGeneticAlgorithmInitializationContainer container from the data obtained from the incoming setup (in properties format, can be gzip compressed).
WekaGeneticHelper	Helper for Weka classes.
WekaGetCapabilities	Retrieves the capabilities of a weka.core.CapabilitiesHandler (eg filter or classifier) and forwards them.
WekaGetInstancesValue	Retrieves a value from a WEKA Instances object. Notes: - date and relational values are forwarded as strings - missing values are output as '?' (without the single quotes) Input/output: - accepts: weka.core.Instances - generates: java.lang.Double java.lang.String
WekaGetInstanceValue	Retrieves a value from a WEKA Instance object. Notes: - date and relational values are forwarded as strings - missing values are output as '?' (without the single quotes) - the 'attribute name' option overrides the 'index' option Input/output: - accepts: weka.core.Instance - generates: java.lang.Double java.lang.String Valid options are:
WekaGOEValueDefinition	Definition for generic WEKA GOE objects.
WekaGraphVisualizer	Displays BayesNet graphs in XML or BIF notation Either displays the contents of a file or an object that implements weka.core.Drawable and generates a BayesNet graph.
WekaHomeEnvironmentModifier	Sets a custom WEKA_HOME environment variable inside the project's home directory.
WekaInstanceBuffer	Can act in two different ways: 1.
WekaInstanceBuffer.Operation	Defines how the buffer actor operates.
WekaInstanceContainer	Encapsulates a Instance object.
WekaInstanceDumper	Dumps weka.core.Instance objects into an ARFF file.
WekaInstanceDumper.OutputFormat	The format to output the data in.
WekaInstanceEvaluator	Adds a new attribute to the data being passed through (normally 'evaluation') and sets the value to the evaluation value returned by the chosen evaluator scheme.
WekaInstanceFileReader	Loads a WEKA dataset from disk with a specified reader and passes on the adams.core.instance.Instance objects.
WekaInstances	Provides further insight into Instance and Instances objects.
WekaInstancesAppend	Creates one large dataset by appending all one after the other.
WekaInstancesDisplay	Actor for displaying a weka.core.Instances object in table format.
WekaInstancesExporter	Exports Weka Instances/Instance objects.
WekaInstancesHistogramRanges	Outputs the ranges generated by adams.data.statistics.ArrayHistogram using the incoming weka.core.Instances object. The actor just uses the internal format (double array) and does not check whether the attributes are actually numeric.
WekaInstancesInfo	Outputs statistics of a weka.core.Instances object. FULL_ATTRIBUTE and FULL_CLASS output a spreadsheet with detailed attribute statistics.
WekaInstancesInfo.InfoType	The type of information to generate.
WekaInstancesMerge	Merges multiple datasets, either from file or using Instances/Instance objects. If no 'ID' attribute is named, then all datasets must contain the same number of rows. Attributes can be excluded from ending up in the final dataset via a regular expression.
WekaInstancesPlot	Actor for plotting one attribute vs another.
WekaInstancesRenderer	Renders Weka Instances/Instance objects.
WekaInstancesStatistic	Generates statistics from a weka.core.Instances object. The actor just uses the internal format (double array) and does not check whether the attributes are actually numeric.
WekaInstancesStatisticDataType	Defines what data to retrieve from an Instances object.
WekaInstancesToSpreadSheet	Generates a spreadsheet from a weka.core.Instances object.
WekaInstanceStreamPlotGenerator	Generates plot containers from a range of attributes of the weka.core.Instance objects being passed through. The generator merely uses the internal data representation for generating the Y value of the plot container.
WekaInstanceToAdamsInstance	Converts weka.core.Instance objects into adams.data.instance.Instance ones.
WekaInstanceToMap	Turns the Weka Instance into a Map, with the attribute names the keys.
WekaInstanceViewer	Actor for displaying adams.data.instance.Instance objects in a graphical way (using the internal format), like the 'Instance Explorer' tool.
WekaInvestigator	Opens the WEKA Investigator.
WekaInvestigatorDataEvent	Event that gets sent when the data in an InvestigatorPanel changes.
WekaInvestigatorDataListener	Interface for classes that get notified about changes in the data in an InvestigatorPanel.
WekaInvestigatorDefinition	Definition for the Weka Investigator props file.
WekaInvestigatorPreferencesPanel	Preferences for the WEKA Investigator.
WekaInvestigatorShortcutsDefinition	Definition for the Weka Investigator shortcuts props file.
WekaLabelIndex	Extended Index class that can use a label name to determine an index of a label as well.
WekaLabelIndexEditor	Editor for WekaLabelIndex objects.
WekaLabelIndexParsing	For parsing WekaLabelIndex options.
WekaLabelRange	Extended Range class that also allows attribute names for specifying attribute positions (names are case-insensitive, just like placeholders for 'first', 'second', etc).
WekaLabelRangeEditor	Editor for WekaLabelRange objects.
WekaLabelRangeParsing	For parsing WekaLabelRange options.
WekaMarginCurve	Actor for displaying margin errors.
WekaMergeInstancesActor	Interface for transformers that merge Weka Instances.
WekaModelContainer	A container for models (e.g., classifier or clusterer) and an optional header of a dataset.
WekaModelReader	Actor for loading a model (classifier or clusterer).
WekaModelWriter	Actor for saving a model (classifier or clusterer) alongside an optional header (i.e., weka.core.Instances object) as file.
WekaMultiExperimenter	Opens the WEKA Multi-Experimenter.
WekaMultiLabelSplitter	Splits a dataset containing multiple class attributes ('multi-label') into separate datasets with only a single class attribute.
WekaNearestNeighborSearch	Outputs the specified number of nearest neighbors for the incoming Weka Instance. The data used for the nearest neighbor search is either obtained from storage.
WekaNearestNeighborSearchContainer	A container for nearest neighbor search (instance and neighborhood).
WekaNewExperiment	Generates a new ADAMS experiment setup.
WekaNewInstance	Creates a new weka.core.Instance-derived object, with all values marked as missing. The class implementing the weka.core.Instance interface needs to have a constructor that takes the number of attributes as sole parameter.
WekaNewInstances	Generates an empty dataset, based on the attribute types and names specified. Nominal attributes are generated with an empty set of labels.
WekaOptionHandlerHelpGenerator	Help generator for OptionHandler.
WekaOptionsConversionPanel	Helper panel that turns Weka commandline strings into quoted strings suitable to be placed into code.
WekaOptionUtils	Helper class for option parsing.
WekaPackageManagerAction	Executes the specified action and forwards the generated output.
WekaPackageManagerAction	Executes the specified action and forwards the generated output.
WekaPackageManagerAction	Applies the selected Weka Package Manager action to the incoming data and forwards the generated output.
WekaPackagesClassPathAugmenter	Returns the classpath augmentations for all the installed WEKA packages.
WekaPackageToMap	Turns the Weka Package into a Map.
WekaPackageUtils	Utility functions for Weka packages.
WekaPluginManagerExtensions	Enables further extensions through Weka's PluginManager.
WekaPredictionContainer	A container for predictions made by a classifier.
WekaPredictionContainerToSpreadSheet	Turns a WEKA prediction container into a SpreadSheet object.
WekaPredictionContainerToSpreadSheet.SortContainer	Helper class for sorting the distribution.
WekaPredictionContainerToSpreadSheet.Sorting	How to sort the distribution.
WekaPredictionsToInstances	Generates weka.core.Instances from the predictions of an Evaluation object.
WekaPredictionsToSpreadSheet	Generates a SpreadSheet object from the predictions of an Evaluation object. See also: adams.flow.transformer.WekaSpreadSheetToPredictions Input/output: - accepts: weka.classifiers.Evaluation adams.flow.container.WekaEvaluationContainer - generates: adams.data.spreadsheet.SpreadSheet Container information: - adams.flow.container.WekaEvaluationContainer: Evaluation, Model, Prediction output, Original indices
WekaPrincipalComponents	Performs principal components analysis on the incoming data and outputs the loadings and the transformed data as spreadsheet array. Automatically filters out attributes that cannot be handled by PCA.
WekaPropertySheetPanelPage	Wizard page that use a PropertySheetPanel for displaying the properties of an object.
WekaPropertySheetPanelPage.CustomPropertySheetPanel	Allowing better access to property sheet panel.
WekaPropertyValueConverter	Handler for WEKA classes.
WekaRandomSplit	Splits a dataset into a training and test set according to a specified split percentage.
WekaRegexToRange	Produces a range string from a regular expression describing attributes.
WekaRegressionModel	Regression model for Weka classifiers.
WekaRegressor	Wraps around a Weka classifier that handles numeric classes (= regression).
WekaRelationName	Deprecated.
WekaRenameRelation	Modifies relation names.
WekaReorderAttributesToReference	Reorders the attributes of the Instance/Instances passing through according to the provided reference dataset (callable actor or reference file). This ensures that the generated data always has the same structure as the reference dataset.
WekaRepeatedCrossValidationEvaluator	Performs repeated cross-validation a classifier on an incoming dataset.
WekaRepeatedCrossValidationOutput	Generates output from the incoming repeated cross-validation data.
WekaSelectDataset	Pops up a file chooser dialog, prompting the user to select one or more datasets.
WekaSelectDatasetPage	Wizard page that allows the user to select a Weka dataset.
WekaSelectMultipleDatasetsPage	Wizard page that allows the user to select multiple datasets.
WekaSelectObjects	Allows the user to select an arbitrary number of Weka objects from the specified class hierarchy using the GenericObjectArray.
WekaSetInstancesValue	Sets a value in a WEKA Instances object. Notes: - relational values cannot be set - '?' (without single quotes) is interpreted as missing value Input/output: - accepts: weka.core.Instances - generates: weka.core.Instances
WekaSetInstanceValue	Sets a value in a WEKA Instance. Notes: - relational values cannot be set - '?' (without single quotes) is interpreted as missing value Input/output: - accepts: weka.core.Instance - generates: weka.core.Instance Valid options are:
WekaSimpleCLI	Opens the WEKA SimpleCLI.
WekaSplitGenerator
WekaSpreadSheetToPredictions	Turns the predictions stored in the incoming spreadsheet (actual and predicted) into a Weka weka.classifiers.Evaluation object. For recreating the predictions of a nominal class, the class distributions must be present in the spreadsheet as well. See also: adams.flow.transformer.WekaPredictionsToSpreadSheet Input/output: - accepts: adams.data.spreadsheet.SpreadSheet - generates: weka.classifiers.Evaluation
WekaStoreInstance	Appends the incoming weka.core.Instance to the dataset in storage.
WekaStreamEvaluator	Evaluates an incremental classifier on a data stream using prequential evaluation (first evaluate, then train).
WekaStreamFilter	Filters Instance objects using the specified filter.
WekaSubsets	Splits the dataset based on the unique values of the specified attribute: all rows with the same unique value form a subset.
WekaSystemProperties	Sets some Weka-specific system properties to improve performance.
WekaTestSetClustererEvaluator	Evaluates a trained clusterer (obtained from input) on the dataset obtained from the callable actor. If a class attribute is set, a classes-to-clusters evaluation is performed automatically Input/output: - accepts: weka.clusterers.Clusterer adams.flow.container.WekaModelContainer - generates: adams.flow.container.WekaClusterEvaluationContainer Container information: - adams.flow.container.WekaModelContainer: Model, Header, Dataset - adams.flow.container.WekaClusterEvaluationContainer: Evaluation, Model, Log-likelohood
WekaTestSetEvaluator	Evaluates a trained classifier (obtained from input) on the dataset obtained from the callable actor.
WekaTestSetEvaluator.EvaluateJob
WekaTextDirectoryReader	Loads all text files in a directory and uses the subdirectory names as class labels.
WekaThresholdCurve	Actor for displaying threshold curves, like ROC or precision/recall.
WekaThresholdCurve.AttributeName	The type of the fields.
WekaTrainAssociator	Trains an associator based on the incoming dataset and outputs the built associator alongside the training header and rules (in a model container)..
WekaTrainAssociator.TrainJob
WekaTrainClassifier	Trains a classifier based on the incoming dataset and outputs the built classifier alongside the training header (in a model container). Incremental training is performed, if the input are weka.core.Instance objects and the classifier implements weka.classifiers.UpdateableClassifier.
WekaTrainClassifier.BatchTrainJob
WekaTrainClusterer	Trains a clusterer based on the incoming dataset and output the built clusterer alongside the training header (in a model container). Incremental training is performed, if the input are weka.core.Instance objects and the clusterer implements weka.clusterers.UpdateableClusterer.
WekaTrainClusterer.BatchTrainJob
WekaTrainTestSetClustererEvaluator	Trains a clusterer on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container). The clusterer setup being used in the evaluation is a callable 'Clusterer' actor. If a class attribute is set, a classes-to-clusters evaluation is performed automatically Input/output: - accepts: adams.flow.container.WekaTrainTestSetContainer - generates: adams.flow.container.WekaClusterEvaluationContainer Container information: - adams.flow.container.WekaTrainTestSetContainer: Train, Test, Seed, FoldNumber, FoldCount - adams.flow.container.WekaClusterEvaluationContainer: Evaluation, Model, Log-likelohood
WekaTrainTestSetContainer	A container for storing train and test set.
WekaTrainTestSetEvaluator	Trains a classifier on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container). The classifier setup being used in the evaluation is a callable 'Classifier' actor.
WekaTrainTestSetEvaluator.EvaluateJob
WekaTreeVisualizer	Displays trees in dot notation.
WekaUnorderedAttributeRange	Extended UnorderedRange class that also allows attribute names for specifying attribute positions (names are case-insensitive, just like placeholders for 'first', 'second', etc).
WekaUnorderedAttributeRangeEditor	A PropertyEditor for WekaAttributeRange objects.
WekaUnorderedAttributeRangeParsing	For parsing WekaUnorderedAttributeRange options.
Workbench	Opens the WEKA Workbench.
WorkspaceHelper	Helper class for loading/saving workspaces.
XGBoost	Classifier implementing XGBoost.
XGBoost.BoosterType	The available types of booster.
XGBoost.FeatureSelector	Available feature selectors.
XGBoost.GrowPolicy	Available grow policy settings.
XGBoost.NormaliseType	Available normalisation-type settings.
XGBoost.Objective	The set of possible learning objectives.
XGBoost.ParamValueProvider	Provides a value suitable as a proxy for the XGBoost parameter system.
XGBoost.Predictor	Available predictors.
XGBoost.ProcessType	Available process-type settings.
XGBoost.SampleType	Available sample-type settings.
XGBoost.TreeMethod	Possible tree-method settings.
XGBoost.Updater	Available updaters.
XGBoost.Verbosity	The possible verbosity levels.
XGBoost.XGBoostParameter	Marks a field as participating in the XGBoost parameter system.
XrffSpreadSheetReader	Reads WEKA datasets in ARFF format and turns them into spreadsheets.
XrffSpreadSheetWriter	Writes a spreadsheet in XRFF file format.
YGradientEPO	Applies the External Parameter Orthogonalization (EPO) algorithm to the data. For more information see: http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#External_Parameter_Orthogonalization_.28EPO.29 Valid options are:
YGradientGLSW	Applies the Generalized Least Squares Weighting (GLSW) algorithm to the data. For more information see: http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#Y-Gradient_GLSW Valid options are: