apply readme simplifications & other tweaks

Author: ablaom

README.md

@@ -11,40 +11,27 @@ Comprehensive documentation is [here](https://juliaai.github.io/LearnAPI.jl/dev/
 
 New contributions welcome. See the [road map](ROADMAP.md).
 
-## Code snippet
+## Synopsis
 
-Configure a machine learning algorithm:
+Package provides for variations and elaborations on the following basic pattern in machine
+learning and statistics:
 
 ```julia
-julia> ridge = Ridge(lambda=0.1)
+model = fit(learner, data)
+predict(model, newdata)
 ```
 
-Inspect available functionality:
+Here `learner` specifies the configuration the algorithm (the hyperparameters) while
+`model` stores learned parameters and any byproducts of algorithm execution.
 
-```
-julia> @functions ridge
-(fit, LearnAPI.learner, LearnAPI.strip, obs, LearnAPI.features, LearnAPI.target, predict, LearnAPI.coefficients)
-```
-
-Train:
-
-```julia
-julia> model = fit(ridge, data)
-```
+## Related packages
 
-Predict:
+- [MLCore.jl](https://github.com/JuliaML/MLCore.jl) ([docs](https://juliaml.github.io/MLUtils.jl/stable/api/#Core-API))
 
-```julia
-julia> predict(model, newdata)[1]
-"virginica"
-```
+- [LearnTestAPI.jl](https://github.com/JuliaAI/LearnTestAPI.jl): Package to test implementations of LearnAPI.jl (but documented here)
 
-Predict a probability distribution ([proxy](https://juliaai.github.io/LearnAPI.jl/dev/kinds_of_target_proxy/#proxy_types) for the target):
+- [LearnDataFrontEnds.jl](https://github.com/JuliaAI/LearnDataFrontEnds.jl): for including flexible, user-friendly, data front ends for LearnAPI.jl implementations ([docs]((https://juliaai.github.io/stable/))
 
-```julia
-julia> predict(model, Distribution(), newdata)[1]
-UnivariateFinite{Multiclass{3}}(setosa=>0.0, versicolor=>0.25, virginica=>0.75)
-```
 
 ## Credits
 

docs/src/index.md

@@ -4,10 +4,10 @@
 <div style="font-size:1.4em;font-weight:bold;">
   <a href="anatomy_of_an_implementation"
     style="color: #389826;">Tutorial</a>           &nbsp;|&nbsp;
-  <a href="reference"
-    style="color: #9558B2;">Reference</a>      &nbsp;|&nbsp;
   <a href="common_implementation_patterns"
     style="color: #9558B2;">Patterns</a>
+  <a href="reference"
+    style="color: #9558B2;">Reference</a>      &nbsp;|&nbsp;
 </div>
 
 <span style="color: #9558B2;font-size:4.5em;">
@@ -87,7 +87,7 @@ Algorithms are free to consume data in any format. However, a method called [`ob
 data_interface) (read as "observations") gives developers the option of providing a
 separate data front end for their algorithms. In this case `obs` gives users and
 meta-algorithms access to an algorithm-specific representation of input data, which is
-also guaranteed to implement a standard interface for accessing individual observations,
+additionally guaranteed to implement a standard interface for accessing individual observations,
 unless the algorithm explicitly opts out. Moreover, the `fit` and `predict` methods will
 also be able to consume these alternative data representations, for performance benefits
 in some situations.
@@ -99,8 +99,8 @@ 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.
 
-Some canned data front ends are provided by the
-[LearnDataFrontEnds.jl](https://juliaai.github.io/LearnAPI.jl/stable/) package.
+Some canned data front ends (implementations of [`obs`](@ref)) are provided by the
+[LearnDataFrontEnds.jl](https://juliaai.github.io/LearnDataFrontEnds.jl/stable/) package.
 
 ## Learning more
 

docs/src/testing_an_implementation.md

@@ -38,6 +38,7 @@ LearnTestAPI.Selector
 LearnTestAPI.FancySelector
 LearnTestAPI.NormalEstimator
 LearnTestAPI.Ensemble
+LearnTestAPI.StumpRegressor
 ```
 
 ## Private methods