Naive automated machine learning

verfasst von

Felix Mohr, Marcel Wever

Abstract

An essential task of automated machine learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset. This problem has been addressed with sophisticated black-box optimization techniques such as Bayesian optimization, grammar-based genetic algorithms, and tree search algorithms. Most of the current approaches are motivated by the assumption that optimizing the components of a pipeline in isolation may yield sub-optimal results. We present Naive AutoML , an approach that precisely realizes such an in-isolation optimization of the different components of a pre-defined pipeline scheme. The returned pipeline is obtained by just taking the best algorithm of each slot. The isolated optimization leads to substantially reduced search spaces, and, surprisingly, this approach yields comparable and sometimes even better performance than current state-of-the-art optimizers.

Externe Organisation(en)

Universidad de la Sabana
Universität Paderborn

Typ

Artikel

Journal

Machine learning

Band

112

Seiten

1131-1170

Anzahl der Seiten

40

ISSN

0885-6125

Publikationsdatum

04.2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Artificial intelligence

Elektronische Version(en)

https://doi.org/10.1007/s10994-022-06200-0 (Zugang: Offen)
https://arxiv.org/abs/2103.10496 (Zugang: Unbekannt)