Merge pull request #192 from alan-turing-institute/dev

update readme

README.md

@@ -1,18 +1,56 @@
 ## MLJBase
 
-The model interface for
-[MLJ](https://github.com/alan-turing-institute/MLJ.jl), a Julia
-machine learning framework.
+Repository for developers that provides core functionality for the
+[MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine
+learning framework.
 
 [![Build Status](https://travis-ci.com/alan-turing-institute/MLJBase.jl.svg?branch=master)](https://travis-ci.com/alan-turing-institute/MLJBase.jl)
 [![Coverage](http://codecov.io/github/alan-turing-institute/MLJBase.jl/coverage.svg?branch=master)](http://codecov.io/github/alan-turing-institute/MLJBase.jl?branch=master)
 
-MLJ is a Julia framework for combining and tuning machine learning
-models. Any machine learning algorithm written in Julia can be used
-with MLJ if it is defined in a package that imports the MLJBase
-module, and implements the model API defined there. For more
-information, see the MLJ document ["Adding Models for General Use"](https://alan-turing-institute.github.io/MLJ.jl/dev/adding_models_for_general_use/).
+[MLJ](https://github.com/alan-turing-institute/MLJ.jl) is a Julia
+framework for combining and tuning machine learning models. This
+repository provides core functionality for MLJ, including:
 
-Requires at least Julia v1.0.
+- completing the functionality for methods defined "minimally" in
+  MLJ's light-weight model interface
+  [MLJModelInterface](https://github.com/alan-turing-institute/MLJModelInterface.jl)
+
+- definition of **machines** and their associated methods, such as
+  `fit!` and `predict`/`transform`
+
+- MLJ's **model composition** interface, including **learning
+  networks** and **pipelines**
+
+- basic utilities for **manipulating data**
+
+- an extension to
+  [Distributions.jl](https://github.com/JuliaStats/Distributions.jl)
+  called `UnivariateFinite` for randomly sampling *labeled*
+  categorical data
+
+- a [small interface](https://alan-turing-institute.github.io/MLJ.jl/dev/evaluating_model_performance/#Custom-resampling-strategies-1) for **resampling strategies** and implementations, including `CV()`, `StratifiedCV` and `Holdout`
+
+- methods for **performance evaluation**, based on those resampling strategies
+
+- **one-dimensional hyperparameter range types**, constructors and
+  associated methods, for use with
+  [MLJTuning](https://github.com/alan-turing-institute/MLJTuning.jl)
+
+- a [small
+  interface](https://alan-turing-institute.github.io/MLJ.jl/dev/performance_measures/#Traits-and-custom-measures-1)
+  for **performance measures** (losses and scores), enabling the
+  integration of the
+  [LossFunctions.jl](https://github.com/JuliaML/LossFunctions.jl)
+  library, user-defined measures, as well as about two dozen natively
+  defined measures.
+
+- integration with [OpenML](https://www.openml.org)
+
+
+Previously MLJBase provided the model interface for integrating third
+party machine learning models into MLJ. That role has now shifted to
+the light-weight
+[MLJModelInterface](https://github.com/alan-turing-institute/MLJModelInterface.jl)
+package.