Merge pull request #270 from FluxML/multitarget-docstring-fix

Fix `l2` loss in `MultitargetNeuralNetworkRegressor` doctring

docs/src/extended_examples/MNIST/notebook.jl

@@ -3,12 +3,18 @@
 # This tutorial is available as a Jupyter notebook or julia script
 # [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/MNIST).
 
-using Pkg #!md
-const DIR = @__DIR__ #!md
-Pkg.activate(DIR) #!md
-Pkg.instantiate() #!md
+# The following code block assumes the current directory contains `Manifest.toml` and
+# `Project.toml` files tested for this demonstration, available
+# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/MNIST).
+# Otherwise, you can try running `using Pkg; Pkg.activate(temp=true)` instead, and
+# manually add the relevant packages to the temporary environment created.
+
+using Pkg
+const DIR = @__DIR__
+Pkg.activate(DIR)
+Pkg.instantiate()
 
-# **Julia version** is assumed to be 1.10.*
+# **Julia version** is assumed to be ≥ 1.10**
 
 using MLJ
 using Flux

docs/src/extended_examples/spam_detection/notebook.jl

@@ -10,9 +10,15 @@
 # **Warning.** This demo includes some non-idiomatic use of MLJ to allow use of the
 # Flux.jl `Embedding` layer. It is not recommended for MLJ beginners.
 
-using Pkg #!md
-Pkg.activate(@__DIR__); #!md
-Pkg.instantiate(); #!md
+# The following code block assumes the current directory contains `Manifest.toml` and
+# `Project.toml` files tested for this demonstration, available
+# [here](https://github.com/FluxML/MLJFlux.jl/tree/dev/docs/src/extended_examples/spam_detection).
+# Otherwise, you can try running `using Pkg; Pkg.activate(temp=true)` instead, and
+# manually add the relevant packages to the temporary environment created.
+
+using Pkg
+Pkg.activate(@__DIR__);
+Pkg.instantiate();
 
 # ### Basic Imports
 using MLJ

src/types.jl

@@ -1190,16 +1190,14 @@ With the learning rate fixed, we can now compute a CV estimate of the performanc
 all data bound to `mach`) and compare this with performance on the test set:
 
 ```julia
-# custom MLJ loss:
-multi_loss(yhat, y) = l2(MLJ.matrix(yhat), MLJ.matrix(y))
 
 # CV estimate, based on `(X, y)`:
-evaluate!(mach, resampling=CV(nfolds=5), measure=multi_loss)
+evaluate!(mach, resampling=CV(nfolds=5), measure=multitarget_l2)
 
 # loss for `(Xtest, test)`:
 fit!(mach) # trains on all data `(X, y)`
 yhat = predict(mach, Xtest)
-multi_loss(yhat, ytest)
+multitarget_l2(yhat, ytest)
 ```
 
 See also