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## Uses of Class
cc.mallet.fst.TransducerEvaluator

Packages that use TransducerEvaluator
cc.mallet.fst | Transducers, including Conditional Random Fields (CRFs).

Uses of TransducerEvaluator in cc.mallet.fst

Subclasses of TransducerEvaluator in cc.mallet.fst
`class` | `CRFWriter` Saves a trained model to specified filename.
`class` | `InstanceAccuracyEvaluator` Reports the percentage of instances for which the entire predicted sequence was correct.
`class` | `LabelDistributionEvaluator` Prints predicted and true label distribution.
`class` | `MultiSegmentationEvaluator` Evaluates a transducer model, computes the precision, recall and F1 scores; considers segments that span across multiple tokens.
`class` | `PerClassAccuracyEvaluator` Determines the precision, recall and F1 on a per-class basis.
`class` | `SegmentationEvaluator`
`class` | `TokenAccuracyEvaluator` Evaluates a transducer model based on predictions of individual tokens.
`class` | `ViterbiWriter` Prints the input instances along with the features and the true and predicted labels to a file.

Methods in cc.mallet.fst with parameters of type TransducerEvaluator
`TransducerTrainer` | `TransducerTrainer.addEvaluator(TransducerEvaluator te)`
`void` | `CRF.evaluate(TransducerEvaluator eval, InstanceList testing)` Deprecated.
`TransducerTrainer` | `TransducerTrainer.removeEvaluator(TransducerEvaluator te)`
`static void` | `SimpleTagger.test(TransducerTrainer tt, TransducerEvaluator eval, InstanceList testing)` Test a transducer on the given test data, evaluating accuracy with the given evaluator
`boolean` | `HMM.train(InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval)`
`boolean` | `MEMMTrainer.train(InstanceList training, InstanceList validation, InstanceList testing, TransducerEvaluator eval, int numIterations, int numIterationsPerProportion, double[] trainingProportions)` Not implemented yet.
`static CRF` | `SimpleTagger.train(InstanceList training, InstanceList testing, TransducerEvaluator eval, int[] orders, java.lang.String defaultLabel, java.lang.String forbidden, java.lang.String allowed, boolean connected, int iterations, double var, CRF crf)` Create and train a CRF model from the given training data, optionally testing it on the given test data.
`boolean` | `CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions)`
`boolean` | `CRFTrainerByLabelLikelihood.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName)` Train a CRF using feature induction to generate conjunctions of features.
`boolean` | `MEMMTrainer.trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions, java.lang.String gainName)` Not implemented yet.

Method parameters in cc.mallet.fst with type arguments of type TransducerEvaluator
`TransducerTrainer` | `TransducerTrainer.addEvaluators(java.util.Collection<TransducerEvaluator> tes)`

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