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Package de.jstacs.classifiers.assessment

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.

See: Description

Package de.jstacs.classifiers.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:

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

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 data sets 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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