teak target_observation_scitype

Author: ablaom

src/traits.jl

@@ -464,21 +464,25 @@ fit_observation_type(::Any) = Union{}
 """
     LearnAPI.target_observation_scitype(algorithm)
 
-Return an upper bound `S` on the scitype of each observation of `LearnAPI.target(data)`,
-where `data` is an admissible argument in the call `fit(algorithm, data)`.
+Return an upper bound `S` on the scitype of each observation of an applicable target
+variable. Specifically:
 
-This interpretation only holds if `LearnAPI.target(algorithm)` is `true`. In any case,
-however, if `algorithm` implements `predict`, then `S` will always be an
-upper bound on the scitype of observations that could be conceivably extracted from the
-output of [`predict`](@ref). For example, suppose we have
+- If `:(LearnAPI.target) in LearnAPI.functions(algorithm)` (i.e., `fit` consumes target
+  variables) then "target" means anything returned by `LearnAPI.target(algorithm, data)`,
+  where `data` is an admissible argument in the call `fit(algorithm, data)`.
+
+- `S` will always be an upper bound on the scitype of observations that could be
+  conceivably extracted from the output of [`predict`](@ref).
+
+To illustate the second case, suppose we have
 
 ```julia
 model = fit(algorithm, data)
 ŷ = predict(model, Sampleable(), data_new)
 ```
 
-Then each sample generated by each "observation" of `ŷ` (a vector of sampleable objects,
-say) will be bound in scitype by `S`.
+Then each individual sample generated by each "observation" of `ŷ` (a vector of sampleable
+objects, say) will be bound in scitype by `S`.
 
 See also See also [`LearnAPI.fit_observation_scitype`](@ref).