SubStrat: A Subset-Based Strategy for Faster AutoML
- URL: http://arxiv.org/abs/2206.03070v1
- Date: Tue, 7 Jun 2022 07:44:06 GMT
- Title: SubStrat: A Subset-Based Strategy for Faster AutoML
- Authors: Teddy Lazebnik, Amit Somech, Abraham Itzhak Weinberg
- Abstract summary: SubStrat is an AutoML optimization strategy that tackles the data size, rather than configuration space.
It wraps existing AutoML tools, and instead of executing them directly on the entire dataset, SubStrat uses a genetic-based algorithm to find a small subset.
It then employs the AutoML tool on the small subset, and finally, it refines the resulted pipeline by executing a restricted, much shorter, AutoML process on the large dataset.
- Score: 5.833272638548153
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Automated machine learning (AutoML) frameworks have become important tools in
the data scientists' arsenal, as they dramatically reduce the manual work
devoted to the construction of ML pipelines. Such frameworks intelligently
search among millions of possible ML pipelines - typically containing feature
engineering, model selection and hyper parameters tuning steps - and finally
output an optimal pipeline in terms of predictive accuracy. However, when the
dataset is large, each individual configuration takes longer to execute,
therefore the overall AutoML running times become increasingly high. To this
end, we present SubStrat, an AutoML optimization strategy that tackles the data
size, rather than configuration space. It wraps existing AutoML tools, and
instead of executing them directly on the entire dataset, SubStrat uses a
genetic-based algorithm to find a small yet representative data subset which
preserves a particular characteristic of the full data. It then employs the
AutoML tool on the small subset, and finally, it refines the resulted pipeline
by executing a restricted, much shorter, AutoML process on the large dataset.
Our experimental results, performed on two popular AutoML frameworks,
Auto-Sklearn and TPOT, show that SubStrat reduces their running times by 79%
(on average), with less than 2% average loss in the accuracy of the resulted ML
pipeline.
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