public class PseudoRefFMeasure extends PseudoFMeasure
symmetricPrecision| Constructor and Description |
|---|
PseudoRefFMeasure() |
| Modifier and Type | Method and Description |
|---|---|
double |
calculate(AMapping predictions,
GoldStandard goldStandard,
double beta)
The method calculates the pseudo reference F-Measure of the machine learning predictions compared to a gold standard for beta = 1 .
|
double |
precision(AMapping predictions,
GoldStandard goldStandard)
The method calculates the pseudo reference precision of the machine learning predictions compared to a gold standard
|
double |
recall(AMapping predictions,
GoldStandard goldStandard)
The method calculates the pseudo reference recall of the machine learning predictions compared to a gold standard
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calculate, getPseudoFMeasuregetUse1To1Mapping, isSymmetricPrecision, isUse1To1Mapping, setSymmetricPrecision, setUse1To1MappingfalseNegative, trueFalsePositive, trueNegativepublic double calculate(AMapping predictions, GoldStandard goldStandard, double beta)
predictions - The predictions provided by a machine learning algorithm.goldStandard - It contains the gold standard (reference mapping) combined with the source and target URIs.beta - this values specifies how F-Measure is biased between precision and recallpublic double recall(AMapping predictions, GoldStandard goldStandard)
recall in class PseudoFMeasurepredictions - The predictions provided by a machine learning algorithmgoldStandard - It contains the gold standard (reference mapping) combined with the source and target URIspublic double precision(AMapping predictions, GoldStandard goldStandard)
precision in class PseudoFMeasurepredictions - The predictions provided by a machine learning algorithmgoldStandard - It contains the gold standard (reference mapping) combined with the source and target URIsCopyright © 2018. All rights reserved.