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AutoMLPipeline.jl: a package that makes it trivial to create complex ML pipeline structures using simple expressions

Authors: Palmes, Paulito;

AutoMLPipeline.jl: a package that makes it trivial to create complex ML pipeline structures using simple expressions

Abstract

Package Features Pipeline API that allows high-level description of processing workflow Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc Symbolic pipeline parsing for easy expression of complex pipeline structures 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

Related Organizations
Keywords

AI, AutoML, ML pipeline optimization, Meta-pipeline, modeling, prediction, classification

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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