weka.classifiers
Interface Classifier
- All Known Implementing Classes:
- AbstractClassifier, AdaBoostM1, AdditiveRegression, ADTree, AODE, AODEsr, AttributeSelectedClassifier, Bagging, BayesianLogisticRegression, BayesNet, BayesNetGenerator, BFTree, BIFReader, CitationKNN, ClassBalancedND, ClassificationViaClustering, ClassificationViaRegression, ComplementNaiveBayes, ConjunctiveRule, CostSensitiveClassifier, CVParameterSelection, Dagging, DataNearBalancedND, DecisionStump, DecisionTable, Decorate, DMNBtext, DTNB, EditableBayesNet, END, EnsembleSelection, FilteredClassifier, FLR, FT, FTInnerNode, FTLeavesNode, FTNode, FTtree, FURIA, GaussianProcesses, GeneralRegression, Grading, GridSearch, GroovyClassifier, HNB, HyperPipes, IB1, IBk, Id3, IsotonicRegression, IteratedSingleClassifierEnhancer, J48, J48graft, JRip, JythonClassifier, KStar, LADTree, LBR, LeastMedSq, LibLINEAR, LibSVM, LinearRegression, LMT, LMTNode, Logistic, LogisticBase, LogitBoost, LWL, M5Base, M5P, M5Rules, MDD, MetaCost, MIBoost, MIDD, MIEMDD, MILR, MINND, MIOptimalBall, MISMO, MISVM, MIWrapper, MultiBoostAB, MultiClassClassifier, MultilayerPerceptron, MultilayerPerceptronCS, MultipleClassifiersCombiner, MultiScheme, NaiveBayes, NaiveBayesMultinomial, NaiveBayesMultinomialUpdateable, NaiveBayesSimple, NaiveBayesUpdateable, NBTree, ND, NeuralNetwork, NNge, OLM, OneClassClassifier, OneR, OrdinalClassClassifier, OSDL, OSDLCore, PaceRegression, ParallelIteratedSingleClassifierEnhancer, ParallelMultipleClassifiersCombiner, PART, PLSClassifier, PMMLClassifier, PreConstructedLinearModel, Prism, RacedIncrementalLogitBoost, RandomCommittee, RandomForest, RandomizableClassifier, RandomizableIteratedSingleClassifierEnhancer, RandomizableMultipleClassifiersCombiner, RandomizableParallelIteratedSingleClassifierEnhancer, RandomizableParallelMultipleClassifiersCombiner, RandomizableSingleClassifierEnhancer, RandomSubSpace, RandomTree, RBFNetwork, RealAdaBoost, Regression, RegressionByDiscretization, REPTree, Ridor, RotationForest, RuleNode, RuleSetModel, SerializedClassifier, SimpleCart, SimpleLinearRegression, SimpleLogistic, SimpleMI, SingleClassifierEnhancer, SMO, SMOreg, SPegasos, Stacking, StackingC, ThresholdSelector, TLD, TLDSimple, TreeModel, UserClassifier, VFI, Vote, VotedPerceptron, WAODE, Winnow, ZeroR
public interface Classifier
Classifier interface. All schemes for numeric or nominal prediction in
Weka implement this interface. Note that a classifier MUST either implement
distributionForInstance() or classifyInstance().
- Version:
- $Revision: 6041 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
buildClassifier
void buildClassifier(Instances data)
throws java.lang.Exception
- Generates a classifier. Must initialize all fields of the classifier
that are not being set via options (ie. multiple calls of buildClassifier
must always lead to the same result). Must not change the dataset
in any way.
- Parameters:
data
- set of instances serving as training data
- Throws:
java.lang.Exception
- if the classifier has not been
generated successfully
classifyInstance
double classifyInstance(Instance instance)
throws java.lang.Exception
- Classifies the given test instance. The instance has to belong to a
dataset when it's being classified. Note that a classifier MUST
implement either this or distributionForInstance().
- Parameters:
instance
- the instance to be classified
- Returns:
- the predicted most likely class for the instance or
Utils.missingValue() if no prediction is made
- Throws:
java.lang.Exception
- if an error occurred during the prediction
distributionForInstance
double[] distributionForInstance(Instance instance)
throws java.lang.Exception
- Predicts the class memberships for a given instance. If
an instance is unclassified, the returned array elements
must be all zero. If the class is numeric, the array
must consist of only one element, which contains the
predicted value. Note that a classifier MUST implement
either this or classifyInstance().
- Parameters:
instance
- the instance to be classified
- Returns:
- an array containing the estimated membership
probabilities of the test instance in each class
or the numeric prediction
- Throws:
java.lang.Exception
- if distribution could not be
computed successfully
getCapabilities
Capabilities getCapabilities()
- Returns the Capabilities of this classifier. Maximally permissive
capabilities are allowed by default. Derived classifiers should
override this method and first disable all capabilities and then
enable just those capabilities that make sense for the scheme.
- Returns:
- the capabilities of this object
- See Also:
Capabilities