class |
AbstractPriorMultiProbabilisticMeasure |
The prior measure depends only on the prediction made.
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class |
ExcessCriterion |
The E/Excess criterion is a prior efficiency measure based on how much the
size of the label set exceeds 1.
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class |
FuzzinessCriterion |
The F/Fuzziness criterion is a prior efficiency measure based on the
difference between the sum of all p-values and the largest p-value.
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class |
MultipleCriterion |
The M/Multiple criterion is a prior efficiency measure based on the size of
the label set.
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class |
MultiProbabilisticLowerBound |
This prior measure returns the predicted lower bound probability.
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class |
MultiProbabilisticUpperBound |
This prior measure returns the predicted upper bound probability.
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class |
NumberCriterion |
The N/Number/AvgC criterion is a prior efficiency measure based on the size
of the label set.
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class |
OneCCriterion |
The OneC is the fraction of predictions that only have one label in their
label set.
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class |
SumCriterion |
The S/Sum criterion is a prior efficiency measure based on the sum of the
p-values.
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class |
UnconfidenceCriterion |
The U/Unconfidence criterion is a prior efficiency measure based on the
(un)confidence of the point prediction.
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