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
fit!
andpredict
/transform
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MLJ's model composition interface, including learning networks and pipelines
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basic utilities for manipulating data
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an extension to Distributions.jl called
UnivariateFinite
for randomly sampling labeled categorical data -
a small interface for resampling strategies and implementations, including
CV()
,StratifiedCV
andHoldout
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methods for performance evaluation, based on those resampling strategies
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one-dimensional hyperparameter range types, constructors and associated methods, for use with MLJTuning
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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.
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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.