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Layered TPOT: Speeding up Tree-based Pipeline Optimization
lib:6ab4c37b460d6a63 (v1.0.0)
Authors: Pieter Gijsbers,Joaquin Vanschoren,Randal S. Olson
ArXiv: 1801.06007
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1801.06007v2
With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.
ArXiv: 1801.06007
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1801.06007v2
With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.
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