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## Package cc.mallet.classify

Classes for training and classifying instances.

See:
Description

Interface Summary
Boostable | This interface is a tag indicating that the classifier attends to the InstanceList.getInstanceWeight() weights when training.
ClassifierTrainer.ByActiveLearning<C extends Classifier> | For active learning, in which this trainer will select certain instances and request that the Labeler instance label them.
ClassifierTrainer.ByIncrements<C extends Classifier> | For various kinds of online learning by batches, where training instances are presented, consumed for learning immediately.
ClassifierTrainer.ByInstanceIncrements<C extends Classifier> | For online learning that can operate on one instance at a time.
ClassifierTrainer.ByOptimization<C extends Classifier> |

Class Summary
AdaBoost | AdaBoost Robert E.
AdaBoostM2 | AdaBoostM2
AdaBoostM2Trainer | This version of AdaBoost can handle multi-class problems.
AdaBoostTrainer | This version of AdaBoost should be used only for binary classification.
BaggingClassifier |
BaggingTrainer | Bagging Trainer.
BalancedWinnow | Classification methods of BalancedWinnow algorithm.
BalancedWinnowTrainer | An implementation of the training methods of a BalancedWinnow on-line classifier.
C45 | A C4.5 Decision Tree classifier.
C45.Node |
C45Trainer | A C4.5 decision tree learner, approximtely.
Classification | The result of classifying a single instance.
Classifier | Abstract parent of all Classifiers.
ClassifierAccuracyEvaluator |
ClassifierEnsemble | Classifer for an ensemble of classifers, combined with learned weights.
ClassifierEnsembleTrainer |
ClassifierEvaluator |
ClassifierTrainer<C extends Classifier> | Each ClassifierTrainer trains one Classifier based on various interfaces for consuming training data.
ClassifierTrainer.Factory<CT extends ClassifierTrainer<? extends Classifier>> | Instances of a Factory know how to create new ClassifierTrainers to apply to new Classifiers.
ConfidencePredictingClassifier |
ConfidencePredictingClassifierTrainer |
DecisionTree | Decision Tree classifier.
DecisionTree.Node |
DecisionTreeTrainer | A decision tree learner, roughly ID3, but only to a fixed given depth in all branches.
DecisionTreeTrainer.Factory |
FeatureConstraintUtil | Utility functions for creating feature constraints that can be used with GE training.
FeatureSelectingClassifierTrainer | Adaptor for adding feature selection to a classifier trainer.
MaxEnt | Maximum Entropy (AKA Multivariate Logistic Regression) classifier.
MaxEntGETrainer | Training of MaxEnt models with labeled features using Generalized Expectation Criteria.
MaxEntL1Trainer |
MaxEntOptimizableByGE |
MaxEntOptimizableByLabelDistribution |
MaxEntOptimizableByLabelLikelihood |
MaxEntTrainer | The trainer for a Maximum Entropy classifier.
MCMaxEnt | Maximum Entropy classifier.
MCMaxEntTrainer | The trainer for a Maximum Entropy classifier.
NaiveBayes | A classifier that classifies instances according to the NaiveBayes method.
NaiveBayesEMTrainer |
NaiveBayesTrainer | Class used to generate a NaiveBayes classifier from a set of training data.
NaiveBayesTrainer.Factory |
RankMaxEnt | Rank Maximum Entropy classifier.
RankMaxEntTrainer | The trainer for a `RankMaxEnt` classifier.
Trial | Stores the results of classifying a collection of Instances, and provides many methods for evaluating the results.
Winnow | Classification methods of Winnow2 algorithm.
WinnowTrainer | An implementation of the training methods of a Winnow2 on-line classifier.

## Package cc.mallet.classify Description

Classes for training and classifying instances. All classification techniques in MALLET are implemented as two classes: a trainer and a classifier. The trainer injests the training data and creates a classifier that holds the parameters set during training. The classifier applies those parameters to an Instance to produce a classification of the Instance.

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