Details
| Original language | English |
|---|---|
| Pages (from-to) | 1131-1170 |
| Number of pages | 40 |
| Journal | Machine learning |
| Volume | 112 |
| Issue number | 4 |
| Publication status | Published - Apr 2023 |
| Externally published | Yes |
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.
Keywords
- Automated Machine Learning, Black-Box Optimization, Data Science
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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In: Machine learning, Vol. 112, No. 4, 04.2023, p. 1131-1170.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Naive automated machine learning
AU - Mohr, Felix
AU - Wever, Marcel
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - Automated Machine Learning
KW - Black-Box Optimization
KW - Data Science
UR - http://www.scopus.com/inward/record.url?scp=85139115238&partnerID=8YFLogxK
U2 - 10.1007/s10994-022-06200-0
DO - 10.1007/s10994-022-06200-0
M3 - Article
AN - SCOPUS:85139115238
VL - 112
SP - 1131
EP - 1170
JO - Machine learning
JF - Machine learning
SN - 0885-6125
IS - 4
ER -