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Packages that use BeanCommon | |
weka.gui.beans |
Uses of BeanCommon in weka.gui.beans |
Classes in weka.gui.beans that implement BeanCommon | |
class |
AbstractDataSink
Abstract class for objects that store instances to some destination. |
class |
AbstractEvaluator
Abstract class for objects that can provide some kind of evaluation for classifier, clusterers etc. |
class |
AbstractTestSetProducer
Abstract class for TestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation. |
class |
AbstractTrainAndTestSetProducer
Abstract base class for TrainAndTestSetProducers that contains default implementations of add/remove listener methods and defualt visual representation. |
class |
AbstractTrainingSetProducer
Abstract class for TrainingSetProducers that contains default implementations of add/remove listener methods and default visual representation |
class |
ClassAssigner
Describe class ClassAssigner here. |
class |
Classifier
Bean that wraps around weka.classifiers |
class |
ClassifierPerformanceEvaluator
A bean that evaluates the performance of batch trained classifiers |
class |
CrossValidationFoldMaker
Bean for splitting instances into training ant test sets according to a cross validation |
class |
CSVDataSink
Data sink that stores instances to a comma separated values (CSV) text file |
class |
Filter
A wrapper bean for Weka filters |
class |
IncrementalClassifierEvaluator
Bean that evaluates incremental classifiers |
class |
PredictionAppender
Bean that can can accept batch or incremental classifier events and produce dataset or instance events which contain instances with predictions appended. |
class |
StripChart
Bean that can display a horizontally scrolling strip chart. |
class |
TestSetMaker
Bean that accepts data sets and produces test sets |
class |
TrainingSetMaker
Bean that accepts a data sets and produces a training set |
class |
TrainTestSplitMaker
Bean that accepts data sets, training sets, test sets and produces both a training and test set by randomly spliting the data |
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