class |
Accuracy |
The class represents the accuracy of the mapping which is defined as the proportion of true results (positive or negative) to the total number
of the population, (T+) + (T-)/(+) + (-)), T+: true positive, T-:True negative(mxn-goldstandard-F+), +: all postitive (gold standard), -: all possible links out of gold standard(mxn-gold)
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class |
APRF |
This class is an abstract class for the Precision, Recall and F-Measure classes.
It contains set of methods that calculate the values of true-positive, false-positive, true-negative and false-negative which
are used by evaluators classes to evaluate the mappings results.
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class |
APseudoPRF |
This class is an abstract class for the Pseudo Precision, Pseudo Recall and Pseudo F-Measure classes.
It extends the abstract class PRF and implements additional methods that sets, gets and checks some flags values required for pseudo-measures.
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class |
AUC |
Quantitative measure representing the area under the curve of ROC (see
here). |
class |
FMeasure |
F-Measure is the weighted average of the precision and recall
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class |
Precision |
It can be defined as the ratio of the retrieved correct results relative to the total number of the retrieved results,i.e.
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class |
PseudoFMeasure |
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
Thereby, not relying on any gold standard.
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class |
PseudoPrecision |
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
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class |
PseudoRecall |
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo F-Measure.
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class |
PseudoRefFMeasure |
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference F-Measure.
Thereby, not relying on any gold standard.
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class |
PseudoRefPrecision |
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Precision.
Thereby, not relying on any gold standard.
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class |
PseudoRefRecall |
Implements a quality measure for unsupervised ML algorihtms, dubbed pseudo Reference Recall.
Thereby, not relying on any gold standard.
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class |
Recall |
It measures how far the algorithm retrieved correct results out of the all existed correct results.
It is defined to be the ratio between the true positive to the total number of correct results whether retrieved or not
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