Repository for developers that provides core functionality for theMLJ machinelearning framework.
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MLJ is a Juliaframework for combining and tuning machine learning models. Thisrepository provides core functionality for MLJ, including:
completing the functionality for methods defined "minimally" inMLJ's light-weight model interfaceMLJModelInterface
definition of machines and their associated methods, such asfit!
and predict
/transform
. Serialization of machines,however, now lives inMLJSerialization.
MLJ's model composition interface, including learningnetworks and pipelines
basic utilities for manipulating data
an extension toDistributions.jlcalled UnivariateFinite
for randomly sampling labeledcategorical 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 andassociated methods, for use withMLJTuning
a smallinterfacefor performance measures (losses and scores), enabling theintegration of theLossFunctions.jllibrary, user-defined measures, as well as about forty nativelydefined measures.
Previously MLJBase provided the model interface for integrating thirdparty machine learning models into MLJ. That role has now shifted tothe light-weightMLJModelInterfacepackage.