All Packages  Class Hierarchy

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M

M_AVERAGE. Static variable in interface weka.classifiers.kstar.KStarConstants
m_col. Variable in class weka.gui.treevisualizer.NamedColor
The actual color object
m_cols. Variable in class weka.gui.treevisualizer.Colors
The array with all the colors input
m_customColour. Variable in class weka.gui.visualize.PlotData2D
M_DELETE. Static variable in interface weka.classifiers.kstar.KStarConstants
Missing value handling mode
m_displayAllPoints. Variable in class weka.gui.visualize.PlotData2D
Display all points (ie.
m_experimentFinished. Variable in class weka.experiment.RemoteExperimentEvent
True if a remote experiment has finished
m_indexVal. Variable in class weka.gui.visualize.AttributePanelEvent
The index for the new attribute
m_logMessage. Variable in class weka.experiment.RemoteExperimentEvent
A log type message
M_MAXDIFF. Static variable in interface weka.classifiers.kstar.KStarConstants
m_messageString. Variable in class weka.experiment.RemoteExperimentEvent
The message
m_name. Variable in class weka.gui.treevisualizer.NamedColor
The name of the color
M_NORMAL. Static variable in interface weka.classifiers.kstar.KStarConstants
m_statusMessage. Variable in class weka.experiment.RemoteExperimentEvent
A status type message
m_useCustomColour. Variable in class weka.gui.visualize.PlotData2D
Custom colour for this plot
m_xChange. Variable in class weka.gui.visualize.AttributePanelEvent
True if the x selection changed
m_yChange. Variable in class weka.gui.visualize.AttributePanelEvent
True if the y selection changed
M5Prime class weka.classifiers.m5.M5Prime.
Class for contructing and evaluating model trees; M5' algorithm.
M5Prime(). Constructor for class weka.classifiers.m5.M5Prime
M5Utils class weka.classifiers.m5.M5Utils.
Class for some small methods used in M5Java
M5Utils(). Constructor for class weka.classifiers.m5.M5Utils
MahalanobisEstimator class weka.estimators.MahalanobisEstimator.
Simple probability estimator that places a single normal distribution over the observed values.
MahalanobisEstimator(Matrix, double, double). Constructor for class weka.estimators.MahalanobisEstimator
Constructor
main(String[]). Static method in class weka.associations.Apriori
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.GainRatioAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.CfsSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.ReliefFAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.AttributeSelection
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.ChiSquaredAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.OneRAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.InfoGainAttributeEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.ConsistencySubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.ClassifierSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.attributeSelection.PrincipalComponents
Main method for testing this class
main(String[]). Static method in class weka.attributeSelection.WrapperSubsetEval
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MetaCost
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Prism
Main method for testing this class
main(String[]). Static method in class weka.classifiers.DecisionTable
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.DecisionStump
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.AdaBoostM1
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.ClassificationViaRegression
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.AttributeSelectedClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.CostMatrix
Tests out creation of a frequency dependent cost matrix from the command line.
main(String[]). Static method in class weka.classifiers.Evaluation
A test method for this class.
main(String[]). Static method in class weka.classifiers.Stacking
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.CheckClassifier
Test method for this class
main(String[]). Static method in class weka.classifiers.CVParameterSelection
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.OneR
Main method for testing this class
main(String[]). Static method in class weka.classifiers.Bagging
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.ThresholdSelector
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.KernelDensity
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.IBk
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.ZeroR
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.RegressionByDiscretization
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.IB1
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.LogitBoost
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.HyperPipes
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Id3
Main method.
main(String[]). Static method in class weka.classifiers.MultiClassClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.MultiScheme
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.UserClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.AdditiveRegression
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.BVDecompose
Test method for this class
main(String[]). Static method in class weka.classifiers.CostSensitiveClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.NaiveBayes
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.SMO
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.Logistic
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.LWR
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.VotedPerceptron
Main method.
main(String[]). Static method in class weka.classifiers.NaiveBayesSimple
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.DistributionMetaClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.VFI
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.LinearRegression
Generates a linear regression function predictor.
main(String[]). Static method in class weka.classifiers.FilteredClassifier
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.adtree.ADTree
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.evaluation.ThresholdCurve
Tests the ThresholdCurve generation from the command line.
main(String[]). Static method in class weka.classifiers.evaluation.CostCurve
Tests the CostCurve generation from the command line.
main(String[]). Static method in class weka.classifiers.evaluation.MarginCurve
Tests the MarginCurve generation from the command line.
main(String[]). Static method in class weka.classifiers.j48.J48
Main method for testing this class
main(String[]). Static method in class weka.classifiers.j48.PART
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.kstar.KStar
Main method for testing this class.
main(String[]). Static method in class weka.classifiers.m5.M5Prime
Main method for M5' algorithm
main(String[]). Static method in class weka.classifiers.neural.NeuralNetwork
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.Cobweb
main(String[]). Static method in class weka.clusterers.SimpleKMeans
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.EM
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.DistributionMetaClusterer
Main method for testing this class.
main(String[]). Static method in class weka.clusterers.ClusterEvaluation
Main method for testing this class.
main(String[]). Static method in class weka.core.Matrix
Main method for testing this class.
main(String[]). Static method in class weka.core.Instances
Main method for this class -- just prints a summary of a set of instances.
main(String[]). Static method in class weka.core.Attribute
Simple main method for testing this class.
main(String[]). Static method in class weka.core.SpecialFunctions
Main method for testing this class.
main(String[]). Static method in class weka.core.Instance
Main method for testing this class.
main(String[]). Static method in class weka.core.SparseInstance
Main method for testing this class.
main(String[]). Static method in class weka.core.BinarySparseInstance
Main method for testing this class.
main(String[]). Static method in class weka.core.Statistics
Main method for testing this class.
main(String[]). Static method in class weka.core.ContingencyTables
Main method for testing this class.
main(String[]). Static method in class weka.core.Range
Main method for testing this class.
main(String[]). Static method in class weka.core.CheckOptionHandler
Main method for using the CheckOptionHandler.

Valid options are:

-W classname
The name of the class implementing an OptionHandler.

main(String[]). Static method in class weka.core.SerializedObject
Test routine, reads an arff file from stdin and measures memory usage (the arff file should have long string attribute values)
main(String[]). Static method in class weka.core.Queue
Main method for testing this class.
main(String[]). Static method in class weka.core.Utils
Main method for testing this class.
main(String[]). Static method in class weka.core.converters.CSVLoader
Main method.
main(String[]). Static method in class weka.core.converters.ArffLoader
Main method.
main(String[]). Static method in class weka.core.converters.C45Loader
Main method for testing this class.
main(String[]). Static method in class weka.core.converters.SerializedInstancesLoader
Main method.
main(String[]). Static method in class weka.estimators.NormalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DiscreteEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KDConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DKConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KKConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.MahalanobisEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.DNConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.KernelEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.NNConditionalEstimator
Main method for testing this class.
main(String[]). Static method in class weka.estimators.PoissonEstimator
Main method for testing this class.
main(String[]). Static method in class weka.experiment.CrossValidationResultProducer
main(String[]). Static method in class weka.experiment.Experiment
Configures/Runs the Experiment from the command line.
main(String[]). Static method in class weka.experiment.PairedTTester
Test the class from the command line.
main(String[]). Static method in class weka.experiment.RemoteExperiment
Configures/Runs the Experiment from the command line.
main(String[]). Static method in class weka.experiment.PairedStats
Tests the paired stats object from the command line.
main(String[]). Static method in class weka.experiment.Stats
Tests the paired stats object from the command line.
main(String[]). Static method in class weka.experiment.RemoteEngine
Main method.
main(String[]). Static method in class weka.experiment.InstanceQuery
Test the class from the command line.
main(String[]). Static method in class weka.experiment.OutputZipper
Main method for testing this class
main(String[]). Static method in class weka.filters.Filter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NullFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.SpreadSubsampleFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.InstanceFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.TimeSeriesTranslateFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.TimeSeriesDeltaFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.SwapAttributeValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.StringToNominalFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.EmptyAttributeFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NormalizationFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.SparseToNonSparseFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.MergeTwoValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AllFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.SplitDatasetFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.CopyAttributesFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.ResampleFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NumericTransformFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NonSparseToSparseFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeTypeFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.ObfuscateFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NominalToBinaryFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.ReplaceMissingValuesFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AddFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeSelectionFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.FirstOrderFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.NumericToBinaryFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.RandomizeFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.MakeIndicatorFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeExpressionFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.AttributeFilter
Main method for testing this class.
main(String[]). Static method in class weka.filters.DiscretizeFilter
Main method for testing this class.
main(String[]). Static method in class weka.gui.ResultHistoryPanel
Tests out the result history from the command line.
main(String[]). Static method in class weka.gui.GenericArrayEditor
Tests out the array editor from the command line.
main(String[]). Static method in class weka.gui.SelectedTagEditor
Tests out the selectedtag editor from the command line.
main(String[]). Static method in class weka.gui.AttributeSelectionPanel
Tests the attribute selection panel from the command line.
main(String[]). Static method in class weka.gui.GenericObjectEditor
Tests out the Object editor from the command line.
main(String[]). Static method in class weka.gui.InstancesSummaryPanel
Tests out the instance summary panel from the command line.
main(String[]). Static method in class weka.gui.PropertySelectorDialog
Tests out the property selector from the command line.
main(String[]). Static method in class weka.gui.LogPanel
Tests out the log panel from the command line.
main(String[]). Static method in class weka.gui.CostMatrixEditor
Tests out the array editor from the command line.
main(String[]). Static method in class weka.gui.GUIChooser
Tests out the GUIChooser environment.
main(String[]). Static method in class weka.gui.AttributeSummaryPanel
Tests out the attribute summary panel from the command line.
main(String[]). Static method in class weka.gui.ListSelectorDialog
Tests out the list selector from the command line.
main(String[]). Static method in class weka.gui.SaveBuffer
Main method for testing this class
main(String[]). Static method in class weka.gui.WekaTaskMonitor
Main method for testing this class
main(String[]). Static method in class weka.gui.SimpleCLI
Method to start up the simple cli
main(String[]). Static method in class weka.gui.experiment.SetupPanel
Tests out the experiment setup from the command line.
main(String[]). Static method in class weka.gui.experiment.DistributeExperimentPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.experiment.RunPanel
Tests out the run panel from the command line.
main(String[]). Static method in class weka.gui.experiment.DatasetListPanel
Tests out the dataset list panel from the command line.
main(String[]). Static method in class weka.gui.experiment.RunNumberPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.experiment.Experimenter
Tests out the experiment environment.
main(String[]). Static method in class weka.gui.experiment.GeneratorPropertyIteratorPanel
Tests out the panel from the command line.
main(String[]). Static method in class weka.gui.experiment.HostListPanel
Tests out the host list panel from the command line.
main(String[]). Static method in class weka.gui.experiment.ResultsPanel
Tests out the results panel from the command line.
main(String[]). Static method in class weka.gui.explorer.PreprocessPanel
Tests out the instance-preprocessing panel from the command line.
main(String[]). Static method in class weka.gui.explorer.ClustererPanel
Tests out the clusterer panel from the command line.
main(String[]). Static method in class weka.gui.explorer.Explorer
Tests out the explorer environment.
main(String[]). Static method in class weka.gui.explorer.AttributeSelectionPanel
Tests out the attribute selection panel from the command line.
main(String[]). Static method in class weka.gui.explorer.ClassifierPanel
Tests out the classifier panel from the command line.
main(String[]). Static method in class weka.gui.explorer.AssociationsPanel
Tests out the Associator panel from the command line.
main(String[]). Static method in class weka.gui.treevisualizer.TreeVisualizer
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.Plot2D
Main method for testing this class
main(String[]). Static method in class weka.gui.visualize.ClassPanel
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.AttributePanel
Main method for testing this class.
main(String[]). Static method in class weka.gui.visualize.LegendPanel
Main method for testing this class
main(String[]). Static method in class weka.gui.visualize.VisualizePanel
Main method for testing this class
main2(String[]). Static method in class weka.core.SerializedObject
Test routine, reads text from stdin and measures memory usage
makeBinaryTipText(). Method in class weka.filters.DiscretizeFilter
Returns the tip text for this property
makeCopies(ASEvaluation, int). Static method in class weka.attributeSelection.ASEvaluation
Creates copies of the current evaluator.
makeCopies(Associator, int). Static method in class weka.associations.Associator
Creates copies of the current associator.
makeCopies(Classifier, int). Static method in class weka.classifiers.Classifier
Creates copies of the current classifier, which can then be used for boosting etc.
makeCopies(Clusterer, int). Static method in class weka.clusterers.Clusterer
Creates copies of the current clusterer.
MakeDecList class weka.classifiers.j48.MakeDecList.
Class for handling a decision list.
MakeDecList(ModelSelection, double, int). Constructor for class weka.classifiers.j48.MakeDecList
Constructor for dec list pruned using C4.5 pruning.
MakeDecList(ModelSelection, int, int). Constructor for class weka.classifiers.j48.MakeDecList
Constructor for dec list pruned using hold-out pruning.
makeDistribution(double, int). Static method in class weka.classifiers.evaluation.NominalPrediction
Convert a single prediction into a probability distribution with all zero probabilities except the predicted value which has probability 1.0.
makeFrequencyDependentMatrix(Instances, double). Static method in class weka.classifiers.CostMatrix
Creates a cost matrix for the class attribute of the supplied instances, where the misclassification costs are higher for misclassifying a rare class as a frequent one.
MakeIndicatorFilter class weka.filters.MakeIndicatorFilter.
Creates a new dataset with a boolean attribute replacing a nominal attribute.
MakeIndicatorFilter(). Constructor for class weka.filters.MakeIndicatorFilter
makeUniformDistribution(int). Static method in class weka.classifiers.evaluation.NominalPrediction
Creates a uniform probability distribution -- where each of the possible classes is assigned equal probability.
makeWeighted(CostMatrix). Method in class weka.classifiers.evaluation.ConfusionMatrix
Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
margin(). Method in class weka.classifiers.evaluation.NominalPrediction
Calculates the prediction margin.
MarginCurve class weka.classifiers.evaluation.MarginCurve.
Generates points illustrating the prediction margin.
MarginCurve(). Constructor for class weka.classifiers.evaluation.MarginCurve
Matchable interface weka.core.Matchable.
Interface to something that can be matched with tree matching algorithms.
Matrix class weka.classifiers.m5.Matrix.
Class for handling a matrix
Matrix class weka.core.Matrix.
Class for performing operations on a matrix of floating-point values.
MATRIX_ON_DEMAND. Static variable in class weka.classifiers.MetaCost
MATRIX_ON_DEMAND. Static variable in class weka.classifiers.CostSensitiveClassifier
MATRIX_SUPPLIED. Static variable in class weka.classifiers.MetaCost
MATRIX_SUPPLIED. Static variable in class weka.classifiers.CostSensitiveClassifier
matrix(). Method in class weka.classifiers.j48.Distribution
Returns matrix with distribution of class values.
Matrix(int, int). Constructor for class weka.classifiers.m5.Matrix
Constructs a matrix
Matrix(int, int). Constructor for class weka.core.Matrix
Constructs a matrix.
Matrix(Reader). Constructor for class weka.core.Matrix
Reads a matrix from a reader.
max. Variable in class weka.experiment.Stats
The maximum value seen, or Double.NaN if no values seen
MAX_SHAPES. Static variable in class weka.gui.visualize.Plot2D
maxBag(). Method in class weka.classifiers.j48.Distribution
Returns index of bag containing maximum number of instances.
maxClass(). Method in class weka.classifiers.j48.Distribution
Returns class with highest frequency over all bags.
maxClass(int). Method in class weka.classifiers.j48.Distribution
Returns class with highest frequency for given bag.
maxGenerationsTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property
maxIndex(double[]). Static method in class weka.core.Utils
Returns index of maximum element in a given array of doubles.
maxIndex(int[]). Static method in class weka.core.Utils
Returns index of maximum element in a given array of integers.
maxIterationsTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
maxModelsTipText(). Method in class weka.classifiers.AdditiveRegression
Returns the tip text for this property
mean. Variable in class weka.experiment.Stats
The mean of values at the last calculateDerived() call
mean(double[]). Static method in class weka.core.Utils
Computes the mean for an array of doubles.
meanAbsoluteError(). Method in class weka.classifiers.Evaluation
Returns the mean absolute error.
meanOrMode(Attribute). Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanOrMode(int). Method in class weka.core.Instances
Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value.
meanPriorAbsoluteError(). Method in class weka.classifiers.Evaluation
Returns the mean absolute error of the prior.
measureExamplesProcessed(). Method in class weka.classifiers.adtree.ADTree
Returns the number of examples "counted".
measureNodesExpanded(). Method in class weka.classifiers.adtree.ADTree
Returns the number of nodes expanded.
measureNumAttributesSelected(). Method in class weka.classifiers.AttributeSelectedClassifier
Additional measure --- number of attributes selected
measureNumIterations(). Method in class weka.classifiers.AdditiveRegression
return the number of iterations (base classifiers) completed
measureNumLeaves(). Method in class weka.classifiers.adtree.ADTree
Calls measure function for leaf size - the number of prediction nodes.
measureNumLeaves(). Method in class weka.classifiers.j48.J48
Returns the number of leaves
measureNumLeaves(). Method in class weka.classifiers.m5.M5Prime
return the number of leaves in the tree
measureNumLinearModels(). Method in class weka.classifiers.m5.M5Prime
return the number of linear models
measureNumPredictionLeaves(). Method in class weka.classifiers.adtree.ADTree
Calls measure function for prediction leaf size - the number of prediction nodes without children.
measureNumRules(). Method in class weka.classifiers.DecisionTable
Returns the number of rules
measureNumRules(). Method in class weka.classifiers.j48.J48
Returns the number of rules (same as number of leaves)
measureNumRules(). Method in class weka.classifiers.j48.PART
Return the number of rules.
measureNumRules(). Method in class weka.classifiers.m5.M5Prime
return the number of rules
Measures class weka.classifiers.m5.Measures.
Class for performance measures
Measures(). Constructor for class weka.classifiers.m5.Measures
Constructs a Measures object which could containing the performance measures
measures(Instances, boolean). Method in class weka.classifiers.m5.Node
Computes performance measures of a tree
measureSelectionTime(). Method in class weka.classifiers.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select the attributes
measuresToString(Measures[], Instances, int, int, String). Method in class weka.classifiers.m5.Node
Converts the performance measures into a string
measureTime(). Method in class weka.classifiers.AttributeSelectedClassifier
Additional measure --- time taken (milliseconds) to select attributes and build the classifier
measureTreeSize(). Method in class weka.classifiers.adtree.ADTree
Calls measure function for tree size - the total number of nodes.
measureTreeSize(). Method in class weka.classifiers.j48.J48
Returns the size of the tree
merge(ADTree). Method in class weka.classifiers.adtree.ADTree
Merges two trees together.
merge(PredictionNode, ADTree). Method in class weka.classifiers.adtree.PredictionNode
Merges this node with another.
mergeAllItemSets(FastVector, int, int). Static method in class weka.associations.ItemSet
Merges all item sets in the set of (k-1)-item sets to create the (k)-item sets and updates the counters.
mergeInstance(Instance). Method in class weka.core.Instance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance). Method in class weka.core.SparseInstance
Merges this instance with the given instance and returns the result.
mergeInstance(Instance). Method in class weka.core.BinarySparseInstance
Merges this instance with the given instance and returns the result.
mergeInstances(Instances, Instances). Static method in class weka.core.Instances
Merges two sets of Instances together.
MergeTwoValuesFilter class weka.filters.MergeTwoValuesFilter.
Merges two values of a nominal attribute.

Valid filter-specific options are:

-C col
The column containing the values to be merged.

MergeTwoValuesFilter(). Constructor for class weka.filters.MergeTwoValuesFilter
MetaCost class weka.classifiers.MetaCost.
This metaclassifier makes its base classifier cost-sensitive using the method specified in

Pedro Domingos (1999).

MetaCost(). Constructor for class weka.classifiers.MetaCost
metricTypeTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
min. Variable in class weka.experiment.Stats
The minimum value seen, or Double.NaN if no values seen
minimizeExpectedCostTipText(). Method in class weka.classifiers.CostSensitiveClassifier
minIndex(double[]). Static method in class weka.core.Utils
Returns index of minimum element in a given array of doubles.
minIndex(int[]). Static method in class weka.core.Utils
Returns index of minimum element in a given array of integers.
minMetricTipText(). Method in class weka.associations.Apriori
Returns the tip text for this property
minProb. Variable in class weka.classifiers.kstar.KStarWrapper
used/reused to hold the smallest transformation probability
minsAndMaxs(Instances, double[][], int). Method in class weka.classifiers.j48.C45Split
Returns the minsAndMaxs of the index.th subset.
minStdDevTipText(). Method in class weka.clusterers.EM
Returns the tip text for this property
MISSING_SHAPE. Static variable in class weka.gui.visualize.Plot2D
MISSING_VALUE. Static variable in interface weka.classifiers.evaluation.Prediction
Constant representing a missing value.
missingCount. Variable in class weka.core.AttributeStats
The number of missing values
missingMergeTipText(). Method in class weka.attributeSelection.SymmetricalUncertAttributeEval
Returns the tip text for this property
missingMergeTipText(). Method in class weka.attributeSelection.GainRatioAttributeEval
Returns the tip text for this property
missingMergeTipText(). Method in class weka.attributeSelection.ChiSquaredAttributeEval
Returns the tip text for this property
missingMergeTipText(). Method in class weka.attributeSelection.InfoGainAttributeEval
Returns the tip text for this property
missingSeperateTipText(). Method in class weka.attributeSelection.CfsSubsetEval
Returns the tip text for this property
missingValue(). Static method in class weka.core.Instance
Returns the double that codes "missing".
MODEL_LINEAR_REGRESSION. Static variable in class weka.classifiers.m5.M5Prime
MODEL_MODEL_TREE. Static variable in class weka.classifiers.m5.M5Prime
MODEL_REGRESSION_TREE. Static variable in class weka.classifiers.m5.M5Prime
ModelSelection class weka.classifiers.j48.ModelSelection.
Abstract class for model selection criteria.
ModelSelection(). Constructor for class weka.classifiers.j48.ModelSelection
momentumTipText(). Method in class weka.classifiers.neural.NeuralNetwork
mouseClicked(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseDragged(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Performs intermediate updates to what the user wishes to do.
mouseEntered(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseExited(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mouseMoved(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Does nothing.
mousePressed(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Determines what action the user wants to perform.
mouseReleased(MouseEvent). Method in class weka.gui.treevisualizer.TreeVisualizer
Performs the final stages of what the user wants to perform.
MultiClassClassifier class weka.classifiers.MultiClassClassifier.
Class for handling multi-class datasets with 2-class distribution classifiers.

Valid options are:

-E num
Sets the error-correction mode.

MultiClassClassifier(). Constructor for class weka.classifiers.MultiClassClassifier
multiply(Matrix). Method in class weka.core.Matrix
Reurns the multiplication of two matrices
multiply(Matrix, int, int, int). Method in class weka.classifiers.m5.Matrix
Reurns the multiplication of two matrices
multiResultsetFull(int, int). Method in class weka.experiment.PairedTTester
Creates a comparison table where a base resultset is compared to the other resultsets.
multiResultsetRanking(int). Method in class weka.experiment.PairedTTester
multiResultsetSummary(int). Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
multiResultsetWins(int). Method in class weka.experiment.PairedTTester
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
MultiScheme class weka.classifiers.MultiScheme.
Class for selecting a classifier from among several using cross validation on the training data.

Valid options from the command line are:

-D
Turn on debugging output.

-S seed
Random number seed (default 1).

-B classifierstring
Classifierstring should contain the full class name of a scheme included for selection followed by options to the classifier (required, option should be used once for each classifier).

-X num_folds
Use cross validation error as the basis for classifier selection.

MultiScheme(). Constructor for class weka.classifiers.MultiScheme
mutationProbTipText(). Method in class weka.attributeSelection.GeneticSearch
Returns the tip text for this property

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