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## Uses of Class
de.jstacs.results.ListResult

Packages that use ListResult
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
de.jstacs.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer
de.jstacs.results | This package provides classes for results and sets of results.

Uses of ListResult in de.jstacs.classifiers.assessment

Methods in de.jstacs.classifiers.assessment that return ListResult
`ListResult` | `ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet... s)` Assesses the contained classifiers.
`ListResult` | `ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, ProgressUpdater pU, DataSet... s)` Assesses the contained classifiers.
`ListResult` | `ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, ProgressUpdater pU, DataSet[][]... s)` Assesses the contained classifiers.
`ListResult` | `KFoldCrossValidation.assessWithPredefinedSplits(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet caaps, ProgressUpdater pU, DataSet[]... splitData)` This method implements a k-fold crossvalidation on previously split data.

Uses of ListResult in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery that return ListResult
`static ListResult` | `MotifDiscoveryAssessment.assess(DataSet truth, DataSet prediction, int maxDiff)` This method computes the nucleotide and site measures.

Uses of ListResult in de.jstacs.results

Methods in de.jstacs.results that return ListResult
`ListResult` | `ListResult.sort(String columnName)` This method enables you to sort the entries of this container by a specified column.

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