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Machine Learning Automation Toolbox (MLaut)

Kazakov, V; Király, FJ; (2019) Machine Learning Automation Toolbox (MLaut). arXiv.org: Ithaca (NY), USA. Green open access

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Abstract

In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large number of datasets. MLaut provides a high-level workflow interface to machine algorithm algorithms, implements a local back-end to a database of dataset collections, trained algorithms, and experimental results, and provides easy-to-use interfaces to the scikit-learn and keras modelling libraries. Experiments are easy to set up with default settings in a few lines of code, while remaining fully customizable to the level of hyper-parameter tuning, pipeline composition, or deep learning architecture. As a principal test case for MLaut, we conducted a large-scale supervised classification study in order to benchmark the performance of a number of machine learning algorithms - to our knowledge also the first larger-scale study on standard supervised learning data sets to include deep learning algorithms. While corroborating a number of previous findings in literature, we found (within the limitations of our study) that deep neural networks do not perform well on basic supervised learning, i.e., outside the more specialized, image-, audio-, or text-based tasks.

Type: Working / discussion paper
Title: Machine Learning Automation Toolbox (MLaut)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://arxiv.org/abs/1901.03678
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10069182
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