MLJBase.jl

MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ, including:

• completing the functionality for methods defined "minimally" in MLJ's light-weight model interface MLJModelInterface

• 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 called UnivariateFinite for randomly sampling labeled categorical data

• a small interface 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

• a small interface for performance measures (losses and scores), enabling the integration of the LossFunctions.jl library, user-defined measures, as well as about two dozen natively defined measures.

• integration with OpenML

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 package.