AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for ica (Independent Component Analysis) and pca (Principal Component Analysis) transformations, respectively, concatenated with the hot-bit encoding (ohe) of categorical features (catf) of a given data for rf (Random Forest) modeling.

Features

  • Symbolic pipeline API for easy expression and high-level description of complex pipeline structures and processing workflow
  • Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc
  • Easily extensible architecture by overloading just two main interfaces: fit! and transform
  • Meta-ensembles that allow composition of ensembles of ensembles (recursively if needed) for robust prediction routines
  • Categorical and numerical feature selectors for specialized preprocessing routines based on types
  • AutoMLPipeline is in the Julia Official package registry

Project Samples

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Categories

Machine Learning

License

MIT License

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Additional Project Details

Programming Language

Julia

Related Categories

Julia Machine Learning Software

Registered

2023-11-10