doc tweaks

Author: ablaom

docs/src/index.md

@@ -92,17 +92,15 @@ then overloading `obs` is completely optional. Plain iteration interfaces, with
 knowledge of the number of observations, can also be specified (to support, e.g., data
 loaders reading images from disk).
 
-## Hooks for adding functionality
-
-A key to enabling toolboxes to enhance LearnAPI.jl algorithm functionality is the
-implementation of two key additional methods, beyond the usual `fit` and
-`predict`/`transform`. Given any training `data` consumed by `fit` (such as `data = (X,
-y)` in the example above) [`LearnAPI.features(algorithm, data)`](@ref input) tells us what
-part of `data` comprises *features*, which is something that can be passed onto to
-`predict` or `transform` (`X` in the example) while [`LearnAPI.target(algorithm,
-data)`](@ref), if implemented, tells us what part comprises the target (`y` in the
-example). By explicitly requiring such methods, we free algorithms to consume data in
-multiple forms, including optimised, algorithm-specific forms, as described above.
+## Hooks adding algorithm-generic functonality
+
+Given any training `data` consumed by `fit` (such as `data = (X, y)` in the example above)
+[`LearnAPI.features(algorithm, data)`](@ref input) tells us what part of `data` comprises
+*features*, which is something that can be passed onto to `predict` or `transform` (`X` in
+the example) while [`LearnAPI.target(algorithm, data)`](@ref), if implemented, tells us
+what part comprises the target (`y` in the example). By explicitly requiring such methods,
+LearnAPI.jl frees algorithms to consume data in multiple forms, including optimised,
+algorithm-specific forms, as described above.
 
 ## Learning more
 

src/fit_update.jl

@@ -36,6 +36,13 @@ overloaded to return `true`.
 
 The signature must include `verbosity`.
 
+If `data` encapsulates a *target* variable, as defined in LearnAPI.jl documentation, then
+[`LearnAPI.target(data)`] must be overloaded to return it. If [`predict`](@ref) or
+[`transform`](@ref) are implemented and consume data, then
+[`LearnAPI.features(data)`](@ref) must return something that can be passed as data to
+these methods. A fallback returns `first(data)` if `data` is a tuple, and `data`
+otherwise`.
+
 The LearnAPI.jl specification has nothing to say regarding `fit` signatures with more than
 two arguments. For convenience, for example, an algorithm is free to implement a slurping
 signature, such as `fit(algorithm, X, y, extras...) = fit(algorithm, (X, y, extras...))` but