For a 0.1.5 release

.github/workflows/CI.yml

@@ -30,7 +30,7 @@ jobs:
         arch:
           - x64
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
       - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}

.github/workflows/documenter.yml

@@ -22,11 +22,11 @@ jobs:
       statuses: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - uses: julia-actions/setup-julia@v2
         with:
           version: '1.10'
-      - uses: julia-actions/cache@v1
+      - uses: julia-actions/cache@v2
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy

Project.toml

@@ -2,7 +2,7 @@ name = "MLJTransforms"
 uuid = "23777cdb-d90c-4eb0-a694-7c2b83d5c1d6"
 authors = ["Essam <essamwisam@outlook.com> and contributors"]
 
-version = "0.1.4"
+version = "0.1.5"
 
 [deps]
 BitBasis = "50ba71b6-fa0f-514d-ae9a-0916efc90dcf"

src/transformers/other_transformers/one_hot_encoder.jl

@@ -214,8 +214,8 @@ Train the machine using `fit!(mach, rows=...)`.
   (`ignore=true`). This default behavior can be modified by the
   `ordered_factor` flag.
 
-- `ordered_factor=false`: when `true`, `OrderedFactor` features are
-  universally excluded
+- `ordered_factor=true`: when `false`, `OrderedFactor` features are
+  left untouched.
 
 - `drop_last=false`: whether to drop the column corresponding to the
   final class of encoded features. For example, a three-class feature

src/transformers/other_transformers/standardizer.jl

@@ -270,9 +270,9 @@ In MLJ or MLJBase, bind an instance `model` to data with
 
 where
 
-- `X`: any Tables.jl compatible table or any abstract vector with
-  `Continuous` element scitype (any abstract float vector). Only
-  features in a table with `Continuous` scitype can be standardized;
+- `X`: any Tables.jl compatible table, or any abstract vector with
+  `Continuous` element scitype (any abstract float vector). Features
+  in a table without `Continuous` scitype will be ignored;
   check column scitypes with `schema(X)`.
 
 Train the machine using `fit!(mach, rows=...)`.
@@ -383,4 +383,4 @@ julia> transform(fit!(machine(stand2, X)), X)
 
 See also [`OneHotEncoder`](@ref), [`ContinuousEncoder`](@ref).
 """
-Standardizer
+Standardizer