See: Description
| Interface | Description |
|---|---|
| LearningExperiment |
The
LearningExperiment interface defines the general functionality
of an object that implements an experiment regarding machine learning
algorithms. |
| LearningExperimentListener |
The
LearningExperimentListener interface defines the functionality
of an object that listens to events from a LearningExperiment. |
| ValidationFoldCreator<InputDataType,FoldDataType> |
The
ValidationFoldCreator interface defines the functionality for
an object that can create a collection of folds for a validation experiment
where a set of data is split into training and testing sets multiple times. |
| Class | Description |
|---|---|
| AbstractLearningExperiment |
The
AbstractLearningExperiment class implements the general
functionality of the LearningExperiment interface, which is mainly
the handling of listeners and firing of events. |
| AbstractValidationFoldExperiment<InputDataType,FoldDataType> |
The
AbstractValidationFoldExperiment class implements a common way
of structuring an experiment around a ValidationFoldCreator object
where the fold creator is used to create each of the individual trials of
the experiment. |
| CrossFoldCreator<DataType> |
The
CrossFoldCreator implements a validation fold creator that
creates folds for a typical k-fold cross-validation experiment. |
| LearnerComparisonExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType> |
The
LearnerComparisonExperiment compares the performance of two
machine learning algorithms to determine (using a statistical test) if the
two algorithms have significantly different performance. |
| LearnerComparisonExperiment.Result<SummaryType> |
Encapsulates the results of the comparison experiment.
|
| LearnerRepeatExperiment<InputDataType,LearnedType,StatisticType,SummaryType> |
Runs an experiment where the same learner is evaluated multiple times on
the same data.
|
| LearnerValidationExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType> |
The
LearnerValidationExperiment class implements an experiment where
a supervised machine learning algorithm is evaluated by applying it to a set
of folds created from a given set of data. |
| LeaveOneOutFoldCreator<DataType> |
The
LeaveOneOutFoldCreator class implements the leave-one-out method
for creating training-testing folds for a cross-validation experiment. |
| OnlineLearnerValidationExperiment<DataType,LearnedType,StatisticType,SummaryType> |
Implements an experiment where an incremental supervised machine learning
algorithm is evaluated by applying it to a set of data by successively
testing on each item and then training on it.
|
| ParallelLearnerValidationExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType> |
Parallel version of the LearnerValidationExperiment class that executes
the validations experiments across available cores and hyperthreads.
|
| RandomByTwoFoldCreator<DataType> |
A validation fold creator that takes a given collection of data and randomly
splits it in half a given number of times, returning two folds for each
split, using one half as training and the other half as testing.
|
| RandomFoldCreator<DataType> |
The
RandomFoldCreator class makes use of a randomized data
partitioner to create a set number of folds for a set of data by passing
the data to the data partitioner multiple times. |
| SupervisedLearnerComparisonExperiment<InputType,OutputType,StatisticType,SummaryType> |
A comparison experiment for supervised learners.
|
| SupervisedLearnerValidationExperiment<InputType,OutputType,StatisticType,SummaryType> |
The
SupervisedLearnerValidationExperiment class extends the
LearnerValidationExperiment class to provide a easy way to create
a learner validation experiment for supervised learning. |