doc tweaks

Author: ablaom

docs/src/predict_transform.md

@@ -8,7 +8,7 @@ transform(model, data...) -> transformed_data
 inverse_transform(model, data...) -> inverted_data
 ```
 
-Methods consuming output `obsdata` of data-preprocessor [`obs`](@ref):
+Methods consuming output, `obsdata`, of data-preprocessor [`obs`](@ref):
 
 ```julia
 obspredict(model, kind_of_proxy, obsdata) -> prediction

docs/src/traits.md

@@ -69,8 +69,8 @@ The following convenience methods are provided but not overloadable by new imple
 |:-----------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:--------|
 | `LearnAPI.name(algorithm)`                           | algorithm type name as string                                                                                 | "PCA"   |
 | `LearnAPI.is_algorithm(algorithm)`                   | `true` if `LearnAPI.functions(algorithm)` is not empty                                                                 | `true`  |
-| [`LearnAPI.predict_output_scitypes(algorithm)`](@ref) | dictionary of upper bounds on the scitype of predictions, keyed on subtypes of [`LearnAPI.KindOfProxy`](@ref) |         |
-| [`LearnAPI.predict_output_types(algorithm)`](@ref)   | dictionary of upper bounds on the type of predictions, keyed on subtypes of [`LearnAPI.KindOfProxy`](@ref)    |         |
+| [`LearnAPI.predict_output_scitype(algorithm)`](@ref) | dictionary of upper bounds on the scitype of predictions, keyed on subtypes of [`LearnAPI.KindOfProxy`](@ref) |         |
+| [`LearnAPI.predict_output_type(algorithm)`](@ref)   | dictionary of upper bounds on the type of predictions, keyed on subtypes of [`LearnAPI.KindOfProxy`](@ref)    |         |
 
 ## Implementation guide
 

src/predict_transform.jl

@@ -1,4 +1,4 @@
-function DOC_IMPLEMENTED_METHODS(name; overloaded=false)
+ function DOC_IMPLEMENTED_METHODS(name; overloaded=false)
     word = overloaded ? "overloaded" : "implemented"
     "If $word, you must include `$name` in the tuple returned by the "*
     "[`LearnAPI.functions`](@ref) trait. "
@@ -49,8 +49,9 @@ DOC_MINIMIZE(func) =
 The first signature returns target or target proxy predictions for input features `data`,
 according to some `model` returned by [`fit`](@ref) or [`obsfit`](@ref). Where supported,
 these are literally target predictions if `kind_of_proxy = LiteralTarget()`, and
-probability density/mass functions if `kind_of_proxy =
-Distribution()`. $DOC_HOW_TO_LIST_PROXIES
+probability density/mass functions if `kind_of_proxy = Distribution()`. List all options
+with [`LearnAPI.kinds_of_proxy(algorithm)`](@ref), where `algorithm =
+LearnAPI.algorithm(model)`.
 
 The shortcut `predict(model, data...) = predict(model, LiteralTarget(), data...)` is also
 provided.
@@ -135,13 +136,15 @@ where `data...` is what the standard [`predict`](@ref) call expects, as in the c
 `predict(model, kind_of_proxy, data...)`. Note `data` is always a tuple, even if `predict`
 has only one data argument. See more at [`obs`](@ref).
 
+
 $(DOC_MUTATION(:obspredict))
 
 If overloaded, you must include both `LearnAPI.obspredict` and `LearnAPI.predict` in the
 list of methods returned by the [`LearnAPI.functions`](@ref) trait.
 
-Each supported `kind_of_proxy` should be listed in the return value of the
-[`LearnAPI.kinds_of_proxy(algorithm)`](@ref) trait.
+An implementation is provided for each kind of target proxy you wish to support. See the
+LearnAPI.jl documentation for options. Each supported `kind_of_proxy` instance should be
+listed in the return value of the [`LearnAPI.kinds_of_proxy(algorithm)`](@ref) trait.
 
 $(DOC_MINIMIZE(:obspredict))
 

src/traits.jl

@@ -91,7 +91,7 @@ functions(::Any) = ()
 """
     LearnAPI.kinds_of_proxy(algorithm)
 
-Returns an tuple of instances, `kind`, for which for which `predict(algorithm, kind,
+Returns an tuple of all instances, `kind`, for which for which `predict(algorithm, kind,
 data...)` has a guaranteed implementation. Each such `kind` subtypes
 [`LearnAPI.KindOfProxy`](@ref). Examples are `LiteralTarget()` (for predicting actual
 target values) and `Distributions()` (for predicting probability mass/density functions).