ClassifierAssessmentthat is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
|ClassifierAssessment<T extends ClassifierAssessmentAssessParameterSet>||
Class defining an assessment of classifiers.
This class is the superclass used by all
This class implements a k-fold crossvalidation.
This class implements a repeated hold-out experiment for assessing classifiers.
This class implements a repeated subsampling experiment.
This class implements a
This class is a special
ClassifierAssessmentthat 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:
RepeatedHoldOutExperimentimplements 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.
Sampled_RepeatedHoldOutExperimentis 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.
KFoldCrossValidationimplements 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.
RepeatedSubSamplingExperimentsubsamples 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.