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## Package de.jstacs.classifier.assessment

This package allows to assess classifiers.

See:
Description

Class Summary
ClassifierAssessment | Class defining an assessment of classifiers.
ClassifierAssessmentAssessParameterSet | This class is the superclass used by all `ClassifierAssessmentAssessParameterSet`s.
KFoldCrossValidation | This class implements a k-fold crossvalidation.
KFoldCVAssessParameterSet | This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess( ... )` of a `KFoldCrossValidation`.
RepeatedHoldOutAssessParameterSet | This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess( ... )` of a `RepeatedHoldOutExperiment`.
RepeatedHoldOutExperiment | This class implements a repeated hold-out experiment for assessing classifiers.
RepeatedSubSamplingAssessParameterSet | This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call method `assess( ... )` of a `RepeatedSubSamplingExperiment`.
RepeatedSubSamplingExperiment | This class implements a repeated subsampling experiment.
Sampled_RepeatedHoldOutAssessParameterSet | This class implements a `ClassifierAssessmentAssessParameterSet` that must be used to call the method `assess( ... )` of a `Sampled_RepeatedHoldOutExperiment`.
Sampled_RepeatedHoldOutExperiment | This class is a special `ClassifierAssessment` that partitions the data of a user-specified reference class (typically the smallest class) and samples non-overlapping for all other classes, so that one gets the same number of sequences (and the same lengths of the sequences) in each train and test dataset.

## Package de.jstacs.classifier.assessment Description

This package allows to assess classifiers.
It contains the class `ClassifierAssessment` that is used as a super-class of all implemented methodologies of an assessment to assess classifiers. In addition it should be used as a super-class of all coming assessments since this class already implements basic patterns like:

- handling of given classifiers
- handling of given models and construction of classifiers using those models
- management of temporary results (In general an assessment is a repeated procedure producing several temporary results that are summarized in terms of means or standard deviations or standard-errors.)
- construction of a summary (mean and standard-error of temporary results)

Further on it contains three implementations of different assessments to assess classifiers. These are:

- Repeated HoldOut Experiment
- Sampled Repeated HoldOut Experiment
- K-Fold Crossvaliation
- Repeated Subsampling Experiment

A `RepeatedHoldOutExperiment` implements the following procedure. For given data-sets it randomly, mutually exclusive partitions the given data-sets into a train-data-set and a test-data-set. Afterwards it uses these data-sets to first train the classifiers and afterwards assess its performance to correctly predict the elements of the test-data-sets. This step is repeated at users will.
A `Sampled_RepeatedHoldOutExperiment` is a special ClassifierAssessment that partitions the data of a user-specified reference class and samples non-overlapping for all other classes, so that one gets the same number of sequences (and the same lengths of the sequences) in each train and test data set.
A `KFoldCrossValidation` implements a k-fold crossvalidation. That is the given data is randomly and mutually exclusive partitioned into k parts. Each of these parts is used once as test-data-set and the remaining k-1 parts are used once as train-data-sets. In each of the k steps the classifiers are trained using the train-data-sets and their performance to correctly predict the elements of the test-data-sets is assessed.
A `RepeatedSubSamplingExperiment` subsamples in each step a train-data-set and a test-data-set from given data. These data-sets may be overlapping. Afterwards the classifiers are trained using the train-data-sets and their performance to predict the elements of the test-data-sets is assessed. This procedure is repeated at users will.
In addition all classes allow to assess classifiers using a set of user-specified test-data-sets and a set of user specified train-data-sets. This methodology allows the user to use test- and train-data-sets that are not automatically generated but user-specified.

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