Repository for developers that provides core functionality for the MLJ machine learning framework.
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
transform. Serialization of machines, however, now lives in MLJSerialization.
MLJ's model composition interface, including learning networks and pipelines
basic utilities for manipulating data
an extension to Distributions.jl called
UnivariateFinitefor randomly sampling labeled categorical data
a small interface for resampling strategies and implementations, including
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 forty natively defined measures.
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.