Uses of Interface
se.hb.jcp.cp.measures.IMeasure

Packages that use IMeasure

Package	Description
se.hb.jcp.cp.measures

Uses of IMeasure in se.hb.jcp.cp.measures

Subinterfaces of IMeasure in se.hb.jcp.cp.measures

Modifier and Type	Interface	Description
interface	IObservedMeasure	An observed measure depends on the prediction made and the true label.
interface	IObservedProbabilisticMeasure	An observed measure depends on the prediction made and the true label.
interface	IPriorMeasure	A prior measure depends only on the prediction made.
interface	IPriorMultiProbabilisticMeasure	A prior measure depends only on the prediction made.

Classes in se.hb.jcp.cp.measures that implement IMeasure

Modifier and Type	Class	Description
class	AbstractPriorMultiProbabilisticMeasure	The prior measure depends only on the prediction made.
class	AbstractSignificanceBasedMeasure	Base class for measures that depend on the significance level.
class	ExcessCriterion	The E/Excess criterion is a prior efficiency measure based on how much the size of the label set exceeds 1.
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.
class	MultipleCriterion	The M/Multiple criterion is a prior efficiency measure based on the size of the label set.
class	MultiProbabilisticLowerBound	This prior measure returns the predicted lower bound probability.
class	MultiProbabilisticUpperBound	This prior measure returns the predicted upper bound probability.
class	NumberCriterion	The N/Number/AvgC criterion is a prior efficiency measure based on the size of the label set.
class	ObservedAccuracy	The Observed Accuracy is the fraction of predictions that include the true label in their label set.
class	ObservedExcessCriterion	The OE/Observed Excess criterion is an observed efficiency measure based on the number of false labels in the label set.
class	ObservedFuzzinessCriterion	The OF/Observed Fuzziness criterion is a prior efficiency measure based on the sum of all p-values for false labels.
class	ObservedMultipleCriterion	The OM/Observed Multiple criterion is an observed efficiency measure based on the number of false labels in the label set.
class	ObservedOneCCriterion	The Observed OneC criterion is the fraction of predictions that only have the true label in their label set.
class	ObservedProbabilityLogLoss	The observed log loss measure depends on the prediction made and the true label.
class	ObservedProbabilitySquareLoss	The observed square loss measure depends on the prediction made and the true label.
class	ObservedUnconfidenceCriterion	The UO/Observed Unconfidence criterion is a prior efficiency measure based on the (un)confidence of the point prediction.
class	OneCCriterion	The OneC is the fraction of predictions that only have one label in their label set.
class	SumCriterion	The S/Sum criterion is a prior efficiency measure based on the sum of the p-values.
class	UnconfidenceCriterion	The U/Unconfidence criterion is a prior efficiency measure based on the (un)confidence of the point prediction.