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Article overview
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Subjectivity in Unsupervised Machine Learning Model Selection | | Wanyi Chen
; Mary L. Cummings
; | | Date: |
1 Sep 2023 | | Abstract: | Model selection is a necessary step in unsupervised machine learning. Despite
numerous criteria and metrics, model selection remains subjective. A high
degree of subjectivity may lead to questions about repeatability and
reproducibility of various machine learning studies and doubts about the
robustness of models deployed in the real world. Yet, the impact of modelers’
preferences on model selection outcomes remains largely unexplored. This study
uses the Hidden Markov Model as an example to investigate the subjectivity
involved in model selection. We asked 33 participants and three Large Language
Models (LLMs) to make model selections in three scenarios. Results revealed
variability and inconsistencies in both the participants’ and the LLMs’
choices, especially when different criteria and metrics disagree. Sources of
subjectivity include varying opinions on the importance of different criteria
and metrics, differing views on how parsimonious a model should be, and how the
size of a dataset should influence model selection. The results underscore the
importance of developing a more standardized way to document subjective choices
made in model selection processes. | | Source: | arXiv, 2309.00201 | | Services: | Forum | Review | PDF | Favorites | |
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