MLJBase.jl
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing machine learning models written in Julia and other languages.
MLJ is released under the MIT licensed and sponsored by the Alan Turing Institute.
This repository, MLJBase, 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!
andpredict
/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 dataa small interface for resampling strategies and implementations, including
CV()
,StratifiedCV
andHoldout
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 many 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 lightweight MLJModelInterface package.