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arXiv · 2007.01977

Lale: Consistent Automated Machine Learning

Guillaume Baudart·Martin Hirzel·Kiran Kate·Parikshit Ram·Avraham Shinnar
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Abstract

Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.

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BibTeXRIS

Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar. 2020-07-04. Lale: Consistent Automated Machine Learning. https://arxiv.org/abs/2007.01977

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