Kinds of Target Proxy
The available kinds of target proxy are classified by subtypes of LearnAPI.KindOfProxy. These types are intended for dispatch only and have no fields.
LearnAPI.KindOfProxy — TypeLearnAPI.KindOfProxyAbstract type whose concrete subtypes T each represent a different kind of proxy for some target variable, associated with some algorithm. Instances T() are used to request the form of target predictions in predict calls.
See LearnAPI.jl documentation for an explanation of "targets" and "target proxies".
For example, Distribution is a concrete subtype of LearnAPI.KindOfProxy and a call like predict(model, Distribution(), Xnew) returns a data object whose observations are probability density/mass functions, assuming algorithm supports predictions of that form.
Run LearnAPI.CONCRETE_TARGET_PROXY_TYPES to list all options.
LearnAPI.IID — TypeLearnAPI.IID <: LearnAPI.KindOfProxyAbstract subtype of LearnAPI.KindOfProxy. If kind_of_proxy is an instance of LearnAPI.IID then, given data constisting of $n$ observations, the following must hold:
ŷ = LearnAPI.predict(model, kind_of_proxy, data...)is data also consisting of $n$ observations.The $j$th observation of
ŷ, for any $j$, depends only on the $j$th observation of the provideddata(no correlation between observations).
See also LearnAPI.KindOfProxy.
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.KindOfProxysubtypes not subtypingLearnAPI.IID