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See:
Description
Class Summary | |
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ClassifierAssessment | Class defining an assessment of classifiers. |
ClassifierAssessmentAssessParameterSet | This class is the super-class 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 RepatedHoldOutExperiment . |
RepeatedHoldOutExperiment | This class implements a repeated holdout experiment for assessing classifiers. |
RepeatedSubSamplingAssessParameterSet | This class implements a ClassifierAssessmentAssessParameterSet that must be used
to call method assess() of a RepatedSubSamplingExperiment . |
RepeatedSubSamplingExperiment | This class implements a repeated subsampling experiment. |
Sampled_RepeatedHoldOutAssessParameterSet | |
Sampled_RepeatedHoldOutExperiment | This class is a special ClassifierAssessment that partitions the data of a reference class and samples non-overlapping for the other classes, so that one get the same number of sequences and the same lengths of the sequences. |
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:
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.
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.
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.
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