Class ChoquisticRelevanceLoss
- java.lang.Object
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- ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure<int[],org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification>
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- ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
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- ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.ChoquisticRelevanceLoss
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- All Implemented Interfaces:
org.api4.java.ai.ml.classification.multilabel.evaluation.loss.IMultiLabelClassificationPredictionPerformanceMeasure,org.api4.java.ai.ml.core.evaluation.supervised.loss.IDeterministicPredictionPerformanceMeasure<int[],org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification>
public class ChoquisticRelevanceLoss extends AMultiLabelClassificationMeasure
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Constructor Summary
Constructors Constructor Description ChoquisticRelevanceLoss(double threshold, IMassFunction measure)ChoquisticRelevanceLoss(IMassFunction measure)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description doubleloss(java.util.List<? extends int[]> expected, java.util.List<? extends org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification> predicted)If this performance measure is originally a score function its score is transformed into a loss by multiplying the score with -1.doublescore(java.util.List<? extends int[]> expected, java.util.List<? extends org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification> predicted)If this performance measure is originally a loss function its loss is transformed into a score by multiplying the loss with -1.-
Methods inherited from class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
getThreshold, getThresholdedPredictionAsSet, listToMatrix, listToRelevanceMatrix, listToThresholdedRelevanceMatrix
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Methods inherited from class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
averageInstanceWiseLoss, averageInstanceWiseScore, checkConsistency, loss, score
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Constructor Detail
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ChoquisticRelevanceLoss
public ChoquisticRelevanceLoss(IMassFunction measure)
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ChoquisticRelevanceLoss
public ChoquisticRelevanceLoss(double threshold, IMassFunction measure)
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Method Detail
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loss
public double loss(java.util.List<? extends int[]> expected, java.util.List<? extends org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification> predicted)Description copied from class:APredictionPerformanceMeasureIf this performance measure is originally a score function its score is transformed into a loss by multiplying the score with -1. (loss=-score).- Specified by:
lossin interfaceorg.api4.java.ai.ml.core.evaluation.supervised.loss.IDeterministicPredictionPerformanceMeasure<int[],org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification>- Overrides:
lossin classAPredictionPerformanceMeasure<int[],org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification>
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score
public double score(java.util.List<? extends int[]> expected, java.util.List<? extends org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification> predicted)Description copied from class:APredictionPerformanceMeasureIf this performance measure is originally a loss function its loss is transformed into a score by multiplying the loss with -1. (score=-loss).- Specified by:
scorein interfaceorg.api4.java.ai.ml.core.evaluation.supervised.loss.IDeterministicPredictionPerformanceMeasure<int[],org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification>- Overrides:
scorein classAPredictionPerformanceMeasure<int[],org.api4.java.ai.ml.classification.multilabel.evaluation.IMultiLabelClassification>
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