Enter the MLJ Universe

MLJBase.jl is a large repository with two main purposes: (i) to give "dummy" methods defined in MLJModelInterface their intended functionality (which depends on third party packages, such as [Tables.jl](https://github.com/JuliaData/Tables.jl), [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) and [CategoricalArrays.jl](https://github.com/JuliaData/CategoricalArrays.jl)); and (ii) provide functionality essential to the MLJ user that has not been relegated to its own "satellite" repository for some reason. See the [MLJBase.jl readme](https://github.com/JuliaAI/MLJBase.jl) for a detailed description of MLJBase's contents.

StatisticalMeasures.jl provides performance measures (metrics) such as losses and scores.

MLJModels.jl hosts the *MLJ model registry*, which contains metadata on all the models the MLJ user can search and load from MLJ. Moreover, it provides the functionality for **loading model code** from MLJ on demand. Finally, it furnishes some commonly used transformers for data pre-processing, such as `ContinuousEncoder` and `Standardizer`.

MLJTuning.jl provides MLJ's `TunedModel` wrapper for hyper-parameter optimization, including the extendable API for tuning strategies, and selected in-house implementations, such as `Grid` and `RandomSearch`.

MLJEnsembles.jl provides MLJ's `EnsembleModel` wrapper, for creating homogeneous model ensembles.

MLJIteration.jl provides the `IteratedModel` wrapper for controlling iterative models (snapshots, early stopping criteria, etc).

FeatureSelection.jl provides models for choosing features, and includes the `RecursiveFeatureElimination` wrapper.

MLJFlow.jl provides integration with the platform-agnostic machine learning tracking tool [MLflow](https://mlflow.org).

OpenML.jl provides integration with the [OpenML](https://www.openml.org) data science exchange platform.

MLJLinearModels.jl provides a wide range of julia-native penalized linear models such as Lasso, Elastic-Net, Robust regression, LAD regression, etc.

MLJFlux.jl an experimental package for gradient-descent models, such as traditional neural-networks, built with [Flux.jl](https://github.com/FluxML/Flux.jl), in MLJ.

ScientificTypesBase.jl is an ultra lightweight package providing "scientific" types, such as `Continuous`, `OrderedFactor`, `Image` and `Table`. It's purpose is to formalize conventions around the scientific interpretation of ordinary machine types, such as `Float32` and `DataFrame`.

ScientificTypes.jl articulates the particular convention for the scientific interpretation of data that MLJ adopts.

StatisticalTraits.jl An ultra lightweight package defining fall-back implementations for a collection of traits possessed by statistical objects, principally models and measures (metrics).

DataScienceTutorials collects tutorials on how to use MLJ, which are deployed [here](https://JuliaAI.github.io/DataScienceTutorials.jl/).

MLJTestInterface provides tests for implementations of the MLJ model interface.

MLJTestIntegration provides tests for the entire MLJ ecosystem. (Called when you run `ENV["MLJ_TEST_INTEGRATION"]="true"; Pkg.test("MLJ")`).