doc tweak

Author: ablaom

docs/src/predict_transform.md

@@ -84,24 +84,29 @@ dimension using distances from the cluster centres.
 
 ### [One-liners combining fit and transform/predict](@id one_liners)
 
-Learners may optionally overload `transform` to apply `fit` first, using the supplied
-data if required, and then immediately `transform` the same data. The same applies to
-`predict`. In that case the first argument of `transform`/`predict` is an *learner*
-instead of the output of `fit`:
+Learners may additionally overload `transform` to apply `fit` first, using the supplied
+data if required, and then immediately `transform` the same data.  In that case the first
+argument of `transform` is an *learner* instead of the output of `fit`:
 
 ```julia
-predict(learner, kind_of_proxy, data) # `fit` implied
 transform(learner, data) # `fit` implied
 ```
 
-For example, if `fit(learner, X)` is defined, then `predict(learner, X)` will be
-shorthand for
+This will be shorthand for 
 
 ```julia
-model = fit(learner, X)
-predict(model, X)
+model = fit(learner, X) # or `fit(learner)` in the static case
+transform(model, X)
+```
+
+The same remarks apply to `predict`, as in 
+
+```julia
+predict(learner, kind_of_proxy, data) # `fit` implied
 ```
 
+LearnAPI.jl does not, however, guarantee the provision of these one-liners.
+
 ## [Reference](@id predict_ref)
 
 ```@docs

docs/src/reference.md

@@ -108,7 +108,7 @@ values; for such learners [`LearnAPI.is_composite`](@ref)`(learner)` must be `tr
 (fallback is `false`). Generally, the keyword constructor provided by
 [`LearnAPI.constructor`](@ref) must provide default values for all properties that are not
 learner-valued. Instead, these learner-valued properties can have a `nothing` default,
-with the constructor throwing an error if the the constructor call does not explicitly
+with the constructor throwing an error if the constructor call does not explicitly
 specify a new value.
 
 Any object `learner` for which [`LearnAPI.functions(learner)`](@ref) is non-empty is
@@ -150,7 +150,7 @@ minimal (but useless) implementation, see the implementation of `SmallLearner`
 
 ### List of methods
 
-- [`fit`](@ref fit_docs): for (i) training or updating learners that generalize to new
+- [`fit`](@ref fit_docs): for (i) training learners that generalize to new
   data; or (ii) wrapping `learner` in an object that is possibly mutated by
   `predict`/`transform`, to record byproducts of those operations, in the special case of
   *non-generalizing* learners (called here [static algorithms](@ref static_algorithms))