Merge pull request #17 from JuliaAI/cifix

Update CI file and resolve error in building documentation

Author: ablaom

.github/workflows/ci.yml

@@ -125,9 +125,9 @@ jobs:
           julia --project=docs -e '
             if ENV["BUILD_DOCS"] == "true"
                 using Documenter: doctest
-                using MLJBase
+                using FeatureSelection
                 @info "attempting to run the doctests"
-                doctest(MLJBase)
+                doctest(FeatureSelection)
             else
                 @info "skipping the doctests"
             end'

docs/Project.toml

@@ -1,11 +1,13 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
+MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 FeatureSelection = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 
 [compat]
 Documenter = "^1.4"
 MLJ = "^0.20"
+MLJDecisionTreeInterface = "^0.4.2"
 StableRNGs = "^1.0"
 julia = "^1.0"

docs/src/index.md

@@ -61,7 +61,7 @@ y = @views(
     ) .+ 20 .* (A[:, 3] .- 0.5).^ 2 .+ 10 .* A[:, 4] .+ 5 * A[:, 5]
 ) # target
 ```
-Now we that we have our data we can create our recursive feature elimination model and 
+Now we that we have our data, we can create our recursive feature elimination model and 
 train it on our dataset
 ```@example example1
 RandomForestRegressor = @load RandomForestRegressor pkg=DecisionTree
@@ -100,7 +100,7 @@ julia> feature_importances(mach)
   :x9 => 1
  :x10 => 1
 ```
-Note that a variable with lower rank has more significance than a variable with higher rank while a variable with higher feature importance is better than a variable with lower feature importance.
+Note that a variable with lower rank has more significance than a variable with higher rank; while a variable with higher feature importance is better than a variable with lower feature importance.
 
 We can view the important features used by our model by inspecting the `fitted_params` 
 object.

src/models/rfe.jl

@@ -46,7 +46,7 @@ eval(:(const RFE{M} =
 This model implements a recursive feature elimination algorithm for feature selection.
 It recursively removes features, training a base model on the remaining features and
 evaluating their importance until the desired number of features is selected.
-
+       
 Construct an instance with default hyper-parameters using the syntax
 `rfe_model = RecursiveFeatureElimination(model=...)`. Provide keyword arguments to override
 hyper-parameter defaults.