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Human vs. supervised machine learning: Who learns patterns faster?

Kühl, Niklas ORCID iD icon 1,2; Goutier, Marc 3; Baier, Lucas 1,2; Wolff, Clemens 1,2; Martin, Dominik ORCID iD icon 1,2
1 Karlsruhe Service Research Institute (KSRI), Karlsruher Institut für Technologie (KIT)
2 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)
3 Technische Universität Darmstadt (TU Darmstadt)


The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features.

Volltext §
DOI: 10.5445/IR/1000152351
Veröffentlicht am 07.11.2022
DOI: 10.48550/arXiv.2012.03661
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 30.11.2020
Sprache Englisch
Identifikator KITopen-ID: 1000152351
Nachgewiesen in arXiv
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