Merge pull request #893 from alan-turing-institute/dev

For a 0.17.1 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.17.0"
+version = "0.17.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -28,7 +28,7 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 CategoricalArrays = "0.8,0.9, 0.10"
 ComputationalResources = "0.3"
 Distributions = "0.21,0.22,0.23, 0.24, 0.25"
-MLJBase = "0.19"
+MLJBase = "0.19.4"
 MLJEnsembles = "0.2"
 MLJIteration = "0.4"
 MLJModels = "0.15"

docs/src/composing_models.md

@@ -98,8 +98,8 @@ then inverse-transforms those predictions to restore them to the
 original scale. (This represents a model we could alternatively build
 using the `TransformedTargetModel` wrapper and a pipeline.) Here we
 have only dynamic operations, labelled blue. The machines are in
-green. Notice that two operations both use `stand`, which stores the
-learned standardization scale parameters. The lower "Training" panel
+green. Notice that two operations both use `box`, which stores the
+learned Box-Cox parameters. The lower "Training" panel
 indicates which nodes are used to train each machine, and what model
 each machine is associated with.