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## Uses of Class
cc.mallet.classify.Classification

Packages that use Classification
cc.mallet.classify | Classes for training and classifying instances.
cc.mallet.pipe | Classes for processing arbitrary data into instances.
cc.mallet.share.upenn | Utilities that currently include a command line wrapper for the maxent classifier.
cc.mallet.types | Fundamental MALLET types, including FeatureVector, Instance, Label etc.

Uses of Classification in cc.mallet.classify

Methods in cc.mallet.classify that return Classification
`Classification` | `MaxEnt.classify(Instance instance)`
`Classification` | `ClassifierEnsemble.classify(Instance instance)`
`Classification` | `Winnow.classify(Instance instance)` Classifies an instance using Winnow's weights
`Classification` | `RankMaxEnt.classify(Instance instance)`
`Classification` | `AdaBoostM2.classify(Instance inst)`
`Classification` | `AdaBoost.classify(Instance inst)`
`Classification` | `BalancedWinnow.classify(Instance instance)` Classifies an instance using BalancedWinnow's weights
`Classification` | `ConfidencePredictingClassifier.classify(Instance instance)`
`Classification` | `BaggingClassifier.classify(Instance inst)`
`Classification` | `C45.classify(Instance instance)`
`Classification` | `DecisionTree.classify(Instance instance)`
`Classification` | `NaiveBayes.classify(Instance instance)` Classify an instance using NaiveBayes according to the trained data.
`Classification` | `MCMaxEnt.classify(Instance instance)`
`abstract Classification` | `Classifier.classify(Instance instance)`
`Classification[]` | `Classifier.classify(Instance[] instances)`
`Classification` | `AdaBoostM2.classify(Instance inst, int numWeakClassifiersToUse)` Classify the given instance using only the first `numWeakClassifiersToUse` classifiers trained during boosting
`Classification` | `AdaBoost.classify(Instance inst, int numWeakClassifiersToUse)` Classify the given instance using only the first `numWeakClassifiersToUse` classifiers trained during boosting
`Classification` | `Classifier.classify(java.lang.Object obj)` Pipe the object through this classifier's pipe, then classify the resulting instance.

Methods in cc.mallet.classify that return types with arguments of type Classification
`java.util.ArrayList<Classification>` | `Classifier.classify(InstanceList instances)`

Methods in cc.mallet.classify with parameters of type Classification
`boolean` | `Trial.add(Classification c)`
`void` | `Trial.add(int index, Classification c)`

Method parameters in cc.mallet.classify with type arguments of type Classification
`boolean` | `Trial.addAll(java.util.Collection<? extends Classification> collection)`
`boolean` | `Trial.addAll(int index, java.util.Collection<? extends Classification> collection)`

Uses of Classification in cc.mallet.pipe

Methods in cc.mallet.pipe that return Classification
`Classification` | `AddClassifierTokenPredictions.TokenClassifiers.classify(Instance instance)`
`Classification` | `AddClassifierTokenPredictions.TokenClassifiers.classify(Instance instance, boolean useOutOfFold)`

Uses of Classification in cc.mallet.share.upenn

Methods in cc.mallet.share.upenn that return Classification
`static Classification[]` | `MaxEntShell.classify(Classifier classifier, java.util.Iterator<Instance> data)` Compute the maxent classifications for unlabeled instances given by an iterator.
`static Classification` | `MaxEntShell.classify(Classifier classifier, java.lang.String[] features)` Compute the maxent classification of an instance.
`static Classification[]` | `MaxEntShell.classify(Classifier classifier, java.lang.String[][] features)` Compute the maxent classifications of an array of instances

Uses of Classification in cc.mallet.types

Methods in cc.mallet.types with parameters of type Classification
`void` | `ROCData.add(Classification classification)` Adds classification results to the ROC data

Constructors in cc.mallet.types with parameters of type Classification
`ExpGain(InstanceList ilist, Classification[] classifications, double gaussianPriorVariance)`
`GradientGain(InstanceList ilist, Classification[] classifications)`
`KLGain(InstanceList ilist, Classification[] classifications)`

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