kinds_of_target_proxy.md

[Kinds of Target Proxy](@id proxy_types)

The available kinds of [target proxy](@ref proxy) are classified by subtypes of LearnAPI.KindOfProxy. These types are intended for dispatch only and have no fields.

LearnAPI.KindOfProxy

LearnAPI.IID

Simple target proxies (subtypes of LearnAPI.IID)

type	form of an observation
LearnAPI.LiteralTarget	same as target observations
LearnAPI.Sampleable	object that can be sampled to obtain object of the same form as target observation
LearnAPI.Distribution	explicit probability density/mass function whose sample space is all possible target observations
LearnAPI.LogDistribution	explicit log-probability density/mass function whose sample space is possible target observations
† LearnAPI.Probability	numerical probability or probability vector
† LearnAPI.LogProbability	log-probability or log-probability vector
† LearnAPI.Parametric	a list of parameters (e.g., mean and variance) describing some distribution
LearnAPI.LabelAmbiguous	collections of labels (in case of multi-class target) but without a known correspondence to the original target labels (and of possibly different number) as in, e.g., clustering
LearnAPI.LabelAmbiguousSampleable	sampleable version of LabelAmbiguous; see Sampleable above
LearnAPI.LabelAmbiguousDistribution	pdf/pmf version of LabelAmbiguous; see Distribution above
LearnAPI.ConfidenceInterval	confidence interval
LearnAPI.Set	finite but possibly varying number of target observations
LearnAPI.ProbabilisticSet	as for Set but labeled with probabilities (not necessarily summing to one)
LearnAPI.SurvivalFunction	survival function
LearnAPI.SurvivalDistribution	probability distribution for survival time
LearnAPI.OutlierScore	numerical score reflecting degree of outlierness (not necessarily normalized)
LearnAPI.Continuous	real-valued approximation/interpolation of a discrete-valued target, such as a count (e.g., number of phone calls)

† Provided for completeness but discouraged to avoid ambiguities in representation.

Table of concrete subtypes of LearnAPI.IID <: LearnAPI.KindOfProxy.

When the proxy for the target is a single object

In the following table of subtypes T <: LearnAPI.KindOfProxy not falling under the IID umbrella, it is understood that predict(model, ::T, ...) is not divided into individual observations, but represents a single probability distribution for the sample space Y^n, where Y is the space the target variable takes its values, and n is the number of observations in data.

type T	form of output of predict(model, ::T, data...)
LearnAPI.JointSampleable	object that can be sampled to obtain a vector whose elements have the form of target observations; the vector length matches the number of observations in data.
LearnAPI.JointDistribution	explicit probability density/mass function whose sample space is vectors of target observations; the vector length matches the number of observations in data
LearnAPI.JointLogDistribution	explicit log-probability density/mass function whose sample space is vectors of target observations; the vector length matches the number of observations in data

Table of LearnAPI.KindOfProxy subtypes not subtyping LearnAPI.IID