CapabilitiesHandler (original) (raw)

• All Known Subinterfaces:
  MultiInstanceCapabilitiesHandler
  All Known Implementing Classes:
  AbstractAssociator, AbstractClusterer, AbstractDensityBasedClusterer, AbstractFileSaver, AbstractSaver, AbstractTimeSeries, AdaBoostM1, Add, AddClassification, AddCluster, AddExpression, AddID, AdditiveRegression, AddNoise, AddValues, ADTree, AllFilter, AODE, AODEsr, Apriori, ArffSaver, ASEvaluation, AttributeSelectedClassifier, AttributeSelection, AttributeSetEvaluator, Bagging, BayesianLogisticRegression, BayesNet, BayesNetGenerator, BFTree, BIFReader, C45PruneableClassifierTree, C45PruneableClassifierTreeG, C45Saver, CachedKernel, Center, CfsSubsetEval, ChangeDateFormat, ChiSquaredAttributeEval, CitationKNN, ClassAssigner, ClassBalancedND, ClassificationViaClustering, ClassificationViaRegression, Classifier, ClassifierSubsetEval, ClassifierTree, ClassOrder, CLOPE, ClusterMembership, Cobweb, ComplementNaiveBayes, ConjunctiveRule, ConsistencySubsetEval, Copy, CostSensitiveASEvaluation, CostSensitiveAttributeEval, CostSensitiveClassifier, CostSensitiveSubsetEval, CSVSaver, CVParameterSelection, Dagging, DatabaseSaver, DataNearBalancedND, DBSCAN, DecisionStump, DecisionTable, Decorate, DiscreteEstimator, DiscreteEstimatorBayes, DiscreteEstimatorFullBayes, Discretize, Discretize, DMNBtext, DTNB, EditableBayesNet, EM, END, Estimator, FarthestFirst, Filter, FilteredAssociator, FilteredAttributeEval, FilteredClassifier, FilteredClusterer, FilteredSubsetEval, FindWithCapabilities, FirstOrder, FPGrowth, FT, FTInnerNode, FTLeavesNode, FTNode, FTtree, GainRatioAttributeEval, GaussianProcesses, GeneralizedSequentialPatterns, GeneralRegression, Grading, GridSearch, HierarchicalClusterer, HNB, HoldOutSubsetEvaluator, HyperPipes, IB1, IBk, Id3, InfoGainAttributeEval, InterquartileRange, IsotonicRegression, IteratedSingleClassifierEnhancer, J48, J48graft, JRip, Kernel, KernelEstimator, KernelFilter, KStar, LADTree, LatentSemanticAnalysis, LBR, LeastMedSq, LibLINEAR, LibSVM, LibSVMSaver, LinearRegression, LMT, LMTNode, Logistic, LogisticBase, LogitBoost, LWL, M5Base, M5P, M5Rules, MahalanobisEstimator, MakeDecList, MakeDensityBasedClusterer, MakeIndicator, MathExpression, MDD, MergeTwoValues, MetaCost, MIBoost, MIDD, MIEMDD, MILR, MINND, MIOptimalBall, MIPolyKernel, MIRBFKernel, MISMO, MISVM, MIWrapper, MultiBoostAB, MultiClassClassifier, MultiFilter, MultiInstanceToPropositional, MultilayerPerceptron, MultipleClassifiersCombiner, MultiScheme, NaiveBayes, NaiveBayesMultinomial, NaiveBayesMultinomialUpdateable, NaiveBayesSimple, NaiveBayesUpdateable, NBTree, NBTreeClassifierTree, ND, NeuralNetwork, NNge, NominalToBinary, NominalToBinary, NominalToString, NonSparseToSparse, NormalEstimator, Normalize, Normalize, NormalizedPolyKernel, NumericCleaner, NumericToBinary, NumericToNominal, NumericTransform, Obfuscate, OneR, OneRAttributeEval, OPTICS, OrdinalClassClassifier, PaceRegression, PART, PartitionedMultiFilter, PKIDiscretize, PLSClassifier, PLSFilter, PMMLClassifier, PoissonEstimator, PolyKernel, PotentialClassIgnorer, PrecomputedKernelMatrixKernel, PreConstructedLinearModel, PredictiveApriori, PrincipalComponents, PrincipalComponents, Prism, PropositionalToMultiInstance, PruneableClassifierTree, Puk, RacedIncrementalLogitBoost, RandomCommittee, RandomForest, RandomizableClassifier, RandomizableClusterer, RandomizableDensityBasedClusterer, RandomizableIteratedSingleClassifierEnhancer, RandomizableMultipleClassifiersCombiner, RandomizableSingleClassifierEnhancer, RandomizableSingleClustererEnhancer, Randomize, RandomProjection, RandomSubset, RandomSubSpace, RandomTree, RBFKernel, RBFNetwork, Regression, RegressionByDiscretization, RELAGGS, ReliefFAttributeEval, Remove, RemoveFolds, RemoveFrequentValues, RemoveMisclassified, RemovePercentage, RemoveRange, RemoveType, RemoveUseless, RemoveWithValues, Reorder, ReplaceMissingValues, REPTree, Resample, Resample, ReservoirSample, Ridor, RotationForest, RuleNode, SerializedClassifier, SerializedInstancesSaver, sIB, SimpleBatchFilter, SimpleCart, SimpleFilter, SimpleKMeans, SimpleLinearRegression, SimpleLogistic, SimpleMI, SimpleStreamFilter, SingleAssociatorEnhancer, SingleClassifierEnhancer, SingleClustererEnhancer, SMO, SMOreg, SMOTE, SparseToNonSparse, SPegasos, SpreadSubsample, Stacking, StackingC, Standardize, StratifiedRemoveFolds, StringKernel, StringToNominal, StringToWordVector, SubsetByExpression, SVMAttributeEval, SVMLightSaver, SwapValues, SymmetricalUncertAttributeEval, Tertius, ThresholdSelector, TimeSeriesDelta, TimeSeriesTranslate, UnsupervisedAttributeEvaluator, UnsupervisedSubsetEvaluator, UserClassifier, VFI, Vote, VotedPerceptron, WAODE, Wavelet, Winnow, WrapperSubsetEval, XMeans, XRFFSaver, ZeroR

public interface CapabilitiesHandler
Classes implementing this interface return their capabilities in regards to datasets.
Version: Revision:1.1Revision: 1.1 Revision:1.1
Author:
FracPete (fracpete at waikato dot ac dot nz)
See Also:
Capabilities

• • Method Summary

  All Methods Instance Methods Abstract Methods

  Modifier and Type	Method and Description
  Capabilities	getCapabilities() Returns the capabilities of this object.

• • Method Detail

     * #### getCapabilities  
     [Capabilities](../../weka/core/Capabilities.html "class in weka.core") getCapabilities()  
     Returns the capabilities of this object.  
     Returns:  
     the capabilities of this object  
     See Also:  
     [Capabilities](../../weka/core/Capabilities.html "class in weka.core")