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re order iid kinds of proxy to put Interpolated second
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src/types.jl

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@@ -20,9 +20,10 @@ See also [`LearnAPI.KindOfProxy`](@ref).
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# Extended help
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| type | form of an observation |
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|:-------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `Point` | same as target observations; may have the interpretation of a 50% quantile, 50% expectile or mode |
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| type | form of an observation |
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|:----------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `Point` | same as target observations; may have the interpretation of a 50% quantile, 50% expectile or mode |
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| `Interpolated` | real-valued approximation/interpolation of a discrete-valued target, such as a count (e.g., number of phone calls) |
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| `Sampleable` | object that can be sampled to obtain object of the same form as target observation |
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| `Distribution` | explicit probability density/mass function whose sample space is all possible target observations |
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| `LogDistribution` | explicit log-probability density/mass function whose sample space is possible target observations |
@@ -42,7 +43,6 @@ See also [`LearnAPI.KindOfProxy`](@ref).
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| `SurvivalDistribution` | probability distribution for survival time |
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| `SurvivalHazardFunction` | hazard function for survival time |
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| `OutlierScore` | numerical score reflecting degree of outlierness (not necessarily normalized) |
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| `Interpolated` | real-valued approximation/interpolation of a discrete-valued target, such as a count (e.g., number of phone calls) |
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¹Provided for completeness but discouraged to avoid [ambiguities in
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representation](https://github.com/alan-turing-institute/MLJ.jl/blob/dev/paper/paper.md#a-unified-approach-to-probabilistic-predictions-and-their-evaluation).

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