Layered TPOT: Speeding up Tree-based Pipeline Optimization

TL;DR

Layered TPOT enhances the efficiency of automated machine learning by evaluating candidate pipelines on progressively larger data subsets, significantly reducing optimization time while maintaining model quality.

Contribution

It introduces a novel layered evaluation approach to TPOT, enabling faster pipeline optimization without sacrificing performance.

Findings

Faster pipeline optimization on large datasets.

Maintains model quality comparable to original TPOT.

Effective use of layered evaluation in AutoML.

Abstract

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.