Update README.md

Author: juliohm

README.md

@@ -26,28 +26,24 @@ train = (feature1=rand(100), feature2=rand(100), target=rand(1:2, 100))
 test = (feature1=rand(20), feature2=rand(20))
 ```
 
-One can train a learning `model` (e.g. `RandomForestClassifier`) with
-the `train` table:
+It is possible train a learning `model` (e.g. `RandomForestClassifier`) with
+the `train` table to approximate a `:target` label and perform predictions
+with the `test` table:
 
 ```julia
 model = RandomForestClassifier()
-
-learn = Learn(train, model, ["feature1","feature2"] => "target")
+learn = Learn(label(train, :target); model)
+preds = learn(test)
 ```
 
-and apply the trained `model` to the `test` table:
-
-```julia
-pred = learn(test)
-```
+The function `label` is used to tag columns of the table with target labels,
+which can be categorical or continuous. All remaining columns are assumed to
+be predictors.
 
 The package exports native Julia models from various packages
 in the ecosystem. It is also possible to use models from the
 [MLJ.jl](https://github.com/JuliaAI/MLJ.jl) stack.
 
-The combination of TableTransforms.jl with StatsLearnModels.jl
-can be thought of as a powerful alternative to MLJ.jl.
-
 [build-img]: https://img.shields.io/github/actions/workflow/status/JuliaML/StatsLearnModels.jl/CI.yml?branch=main&style=flat-square
 [build-url]: https://github.com/JuliaML/StatsLearnModels.jl/actions