update docs

alan-turing-institute · Aug 13, 2019 · 4c4428a · 4c4428a
1 parent 8932f45
commit 4c4428a
Show file tree

Hide file tree

Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/docs/src/adding_models_for_general_use.md b/docs/src/adding_models_for_general_use.md
@@ -214,7 +214,7 @@ MLJBase.input_is_multivariate(::Type{<:SomeSupervisedModel}) = false
 ```
 
 The target `y` is always an `AbstractVector` (see the discussion in
-[Getting Started](@ref)). For multivariate or sequence-valued
+[Getting Started](index.md). For multivariate or sequence-valued
 targets, a `target_scitype_union` declaration is required. This is
 discussed under [Trait declarations](@ref) below, which also describes how
 to constrain the element types of data.
@@ -443,7 +443,7 @@ if they are omitted (and your model is registered) then a general user
 may attempt to use your model with inappropriately typed data.
 
 The trait functions `input_scitype_union` and `target_scitype_union`
-take scientific data types as values (see [Getting Started](@ref) for
+take scientific data types as values (see [Getting Started](index.md) for
 scitype basics). These types are organized in the following hierarchy:
 
 ![](scitypes.png)
@@ -546,7 +546,7 @@ defines a fallback for `update` which just calls `fit`. For context,
 see [MLJ Internals](internals.md).
 
 Learning networks wrapped as models constitute one use-case (see
-[Learning Networks](@ref)): one would like each component model to be
+[Learning Networks](index.md)): one would like each component model to be
 retrained only when hyperparameter changes "upstream" make this
 necessary. In this case MLJ provides a fallback (specifically, the
 fallback is for any subtype of `SupervisedNetwork =

diff --git a/docs/src/evaluating_model_performance.md b/docs/src/evaluating_model_performance.md
@@ -9,7 +9,7 @@ their own list of train/evaluation pairs of row indices for
 resampling, or define their own re-usable resampling strategies.
 
 For simultaneously evaluating *multiple* models and/or data
-sets, see [Benchmarking](@benchmarking).
+sets, see [Benchmarking](benchmarking.md).
 
 
 ### Evaluating against a single measure
@@ -78,7 +78,7 @@ evaluate!(mach,
 ```
 
 Or define their own re-usable `ResamplingStrategy` objects, - see
-[Custom resampling strategies ](@ref) below.
+[Custom resampling strategies](@ref) below.
 
 
 ### Resampling strategies