All Classes Interface Summary Exception Summary
| Class |
Description |
| ContainsNonNumericAttributesException |
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| DatasetCreationException |
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| DatasetTraceInstructionFailedException |
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| IActiveLearningPoolProvider<I extends ILabeledInstance> |
Provides a sample pool for pool-based active learning.
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| IAnalyticalLearningCurve |
Added some analytical functions to a learning curve.
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| IAttribute |
Wrapper interface for attribute types.
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| IAttributeValue |
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| IBatchLossFunction<Y> |
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| IBatchPredictionAlgorithm<Y,I extends IInstance> |
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| ICategoricalAttribute |
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| ICategoricalAttributeValue |
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| IClassificationLossFunction |
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| IClassifier<I extends ILabeledInstance,D extends ILabeledDataset<I>> |
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| IClusterer |
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| IDataset<I extends IInstance> |
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| IDatasetDeserializer<D> |
A dataset deserializer reads in the contents of a file to return it as a dataset object.
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| IDatasetSplitter<I extends IInstance,D extends IDataset<I>> |
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| IDatasetTraceInstruction<I extends IInstance,D extends IDataset<I>> |
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| IDataSource<I extends IInstance> |
The general dataset interface.
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| IDatsetSerializer<D> |
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| IDyad |
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| IDyadRanker |
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| IDyadRankingDataset |
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| IDyadRankingDataSource |
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| IDyadRankingInstance |
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| IFittable<I extends IInstance,D extends IDataSource<I>> |
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| IFittablePredictor<I extends IInstance,D extends IDataSource<I>> |
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| IGenericPredictionAlgorithm<Y,I> |
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| IInstance |
Instances implementing this interface have a feature description of the type X.
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| IInstanceSchema |
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| IInstanceSchemaHandler |
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| ILabeledDataset<I extends ILabeledInstance> |
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| ILabeledDataSource<I extends ILabeledInstance> |
The supervised dataset is a list (ordered collection) of instances.
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| ILabeledInstance |
Interface of an instance that has a target value.
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| ILabeledInstanceSchema |
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| ILabeledInstanceSchemaHandler |
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| ILabelRanker |
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| ILabelRankingDataset |
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| ILabelRankingDataSource |
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| ILabelRankingInstance |
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| ILabelRankingPrediction |
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| ILearnerConfigHandler |
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| ILearningCurve |
Interface for the result of an learning curve extrapolation.
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| ILossFunction<Y> |
A loss function is defined for a domain quantizing the error of an elment in a domain D compared to the ground truth.
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| IMLModel |
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| IMultiLabelAttribute |
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| IMultiLabelAttributeValue |
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| IMultiLabelClassificationDataset |
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| IMultiLabelClassificationDataSource |
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| IMultiLabelClassificationInstance |
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| IMultiLabelClassificationLossFunction |
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| IMultiLabelClassifier |
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| IMultiLabelSet |
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| INumericAttribute |
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| INumericAttributeValue |
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| INumericEncodingAttribute |
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| IObjectAttribute<O> |
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| IObjectAttributeValue<O> |
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| IOrdinalAttribute |
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| IOrdinalAttributeValue |
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| IPrediction |
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| IPredictionAlgorithm<Y,I extends IInstance> |
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| IPredictionBatch |
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| IPredictor<I extends IInstance,D extends IDataSource<I>> |
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| IProbabilisticPredictor |
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| IRanker<O,I extends IRankingInstance<O>,D extends IRankingDataset<O,I>> |
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| IRanking<O> |
A ranking is a function mapping assigning each object of a set of objects a rank, i.e. a
number between 1 and the total number of objects.
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| IRankingAttribute<O> |
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| IRankingAttributeValue<O> |
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| IRankingDataset<O,I extends IRankingInstance<O>> |
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| IRankingDataSource<O,I extends IRankingInstance<O>> |
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| IRankingInstance<O> |
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| IRankingLossFunction |
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| IRankingPrediction |
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| IRegressionDataset |
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| IRegressionDataSource |
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| IRegressionInstance |
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| IRegressor |
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| ISamplingAlgorithm<I extends IInstance,D extends IDataset<I>> |
Interface for sampling algorithms.
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| ISelectiveSamplingStrategy<I> |
A strategy for selective sampling.
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| ISingleLabelClassificationBatchLossFunction |
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| ISingleLabelClassificationDataset |
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| ISingleLabelClassificationDataSource |
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| ISingleLabelClassificationInstance |
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| ISingleLabelClassificationPrediction |
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| ISingleLabelClassifier |
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| IStringAttribute |
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| IStringAttributeValue |
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| ISupervisedDatasetSplitter<I extends ILabeledInstance,D extends ILabeledDataset<I>> |
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| ISupervisedFitAlgorithm<I extends ILabeledInstance,D extends ILabeledDataSource<I>,M extends IMLModel> |
A fit algorithm can be used to induce a IMLModel from a supervised data
source. |
| ISupervisedLearner<I extends ILabeledInstance,D extends ILabeledDataSource<I>> |
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| ISupervisedSamplingAlgorithm<I extends ILabeledInstance,D extends ILabeledDataset<I>> |
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| ITimeseriesAttribute<Y> |
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| ITimeseriesAttributeValue<Y> |
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| IUnsupervisedFitAlgorithm<X,I extends IInstance,D extends IDataSource<I>,M extends IMLModel> |
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| IUnsupervisedLearner<I extends IInstance,D extends IDataSource<I>> |
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| LearnerConfigurationFailedException |
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| NoValidAttributeValueException |
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| PredictionException |
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| SplitFailedException |
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| TrainingException |
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