Update the manual

docs/make.jl

@@ -92,7 +92,7 @@ pages = [
         "Learning Networks" => "learning_networks.md",
     ],
     "Third Party Tools" => [
-        "Logging Workflows using MLflow" => "logging_workflows.md",
+        "Logging Workflows" => "logging_workflows.md",
         "Third Party Packages" => "third_party_packages.md",
     ],
     "Customization and Extension" => [

docs/src/logging_workflows.md

@@ -1,13 +1,28 @@
 # Logging Workflows
 
-## MLflow integration
+Currently the following workflows can log their outcomes to an external machine learning
+tracking platform, such as [MLflow](https://mlflow.org) (see [MLflow](@ref) below):
 
-[MLflow](https://mlflow.org) is a popular, language-agnostic, tool for externally logging
-the outcomes of machine learning experiments, including those carried out using MLJ.
+- Estimating model performance using [`evaluate`](@ref)/[`evaluate!`](@ref).
+
+- Model tuning, using the `TunedModel` wrapper, as described under [Tuning Models](@ref).
+
+To enable logging one must create a `logger` object for the relevant tracking platform,
+and either:
+
+- Provide `logger` as an explicit keyword argument in the workflow, as in `evaluate(...;
+  logger=...)` or `TunedModel(...; logger=...)`; or
+
+- Set a global default logger with the call [`default_logger(logger)`](@ref).
 
 MLJ logging examples are given in the [MLJFlow.jl](https://github.com/JuliaAI/MLJFlow.jl)
-documentation. MLJ includes and re-exports all the methods of MLJFlow.jl, so there is no
-need to import MLJFlow.jl if `using MLJ`.
+documentation.
+x
+
+### Supported tracking platforms
+
+- [MLflow](@ref) (natively supported: MLJ re-exports `MLJFlow.Logger(...)`)
+
 
 !!! warning
 

docs/src/target_transformations.md

@@ -69,9 +69,6 @@ X, y = @load_boston
 evaluate(ridge3, X, y, measure=l1)
 ```
 
-Without the log transform (ie, using `ridge`) we get the poorer mean absolute error,
-`l1`, of 3.9.
-
 ```@docs
 TransformedTargetModel
 ```