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MLJBase

Repository for developers that provides core functionality for the MLJ machine learning framework.

Branch	Julia	Build	Coverage
master	v1
dev	v1

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 (/src/interface)

• definition of machines and their associated methods, such as fit! and predict/transform (src/machines). Serialization of machines, however, now lives in MLJSerialization.

• MLJ's model composition interface, including learning networks, pipelines, stacks, target transforms (/src/composition)

• basic utilities for manipulating datasets and for synthesizing datasets (src/data)

• a small interface for resampling strategies and implementations, including CV(), StratifiedCV and Holdout (src/resampling.jl)

• methods for performance evaluation, based on those resampling strategies (src/resampling.jl)

• one-dimensional hyperparameter range types, constructors and associated methods, for use with MLJTuning (src/hyperparam)

• a small interface for performance measures (losses and scores), implementation of about 60 such measures, including integration of the LossFunctions.jl library (src/measures). To be migrated into separate package in the near future.