Merge branch 'master' of https://github.com/JuliaAI/MLJEnsembles.jl

.github/workflows/CI.yml

@@ -1,15 +1,13 @@
 name: CI
+env:
+  CODECOV_TOKEN: 'b04186a9-0742-481a-be3e-e7457120db3f'
 
 on:
-  pull_request:
-    branches:
-      - master
-      - dev
   push:
-    branches:
-      - master
-      - dev
-    tags: '*'
+    branches: "dev"
+    tags: ["*"]
+  pull_request:
+  release:
 
 jobs:
   test:
@@ -45,5 +43,10 @@ jobs:
             ${{ runner.os }}-test-${{ env.cache-name }}-
             ${{ runner.os }}-test-
             ${{ runner.os }}-
-      - uses: julia-actions/julia-buildpkg@v1
-      - uses: julia-actions/julia-runtest@v1
+      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-runtest@latest
+      - uses: julia-actions/[email protected]
+        continue-on-error: true
+      - uses: julia-actions/[email protected]
+        continue-on-error: true
+

README.md

@@ -0,0 +1,49 @@
+# MLJEnsembles.jl 
+
+[![Build status](https://github.com/JuliaAI/MLJEnsembles.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/MLJEnsembles.jl/actions) [![codecov.io](http://codecov.io/github/JuliaAI/MLJEnsembles.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaAI/MLJEnsembles.jl?branch=master) 
+
+A package to create bagged homogeneous ensembles of
+machine learning models using the
+[MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) machine
+learning framework.
+
+For combining models in more general ways, see the [Composing
+Models](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
+section of the MLJ manual.
+
+
+## Installation
+
+No installation is necessary when using MLJ, which is installed like this:
+
+```julia
+using Pkg
+Pkg.add("MLJ")\
+using MLJ
+```
+
+Alternatively, for a "minimal" installation:
+
+```julia
+using Pkg
+Pkg.add("MLJBase")
+Pkg.add("MLJEnsembles")
+using MLJBase, MLJEnsembles
+```
+
+In this case you will also need to load code defining an atomic model
+to ensemble. The easiest way to do this is run `Pkg.add("MLJModels");
+using MLJModels` and use the `@load` macro.  See the [Loading Model
+Code](https://alan-turing-institute.github.io/MLJ.jl/dev/loading_model_code/)
+of the MLJ manual for this and other possibilities.
+
+
+## Sample usage
+
+See [Data Science Tutorials](https://alan-turing-institute.github.io/DataScienceTutorials.jl/getting-started/ensembles/).
+
+
+## Documentation
+
+See the [MLJ manual](https://alan-turing-institute.github.io/MLJ.jl/dev/homogeneous_ensembles/#Homogeneous-Ensembles).
+