SubStrat: A Subset-Based Strategy for Faster AutoML

TL;DR

SubStrat is a novel AutoML optimization strategy that reduces runtime by selecting representative data subsets using a genetic algorithm, maintaining high accuracy while significantly decreasing execution time.

Contribution

It introduces a data subset selection approach for AutoML that improves efficiency without sacrificing much accuracy, wrapping existing AutoML tools.

Findings

Reduces AutoML runtime by 79% on average.

Maintains less than 2% accuracy loss.

Effective on Auto-Sklearn and TPOT.

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.