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An increasing number of publications present the joint application of design of experiments (DOE) and machine learning (ML) as a methodology to collect and analyze data on a specific industrial phenomenon. However, the literature shows that the choice of the design for data collection and model for data analysis is often not driven by statistical or algorithmic advantages, thus there is a lack of studies which provide guidelines on what designs and ML models to jointly use for data collection and analysis. This article discusses the choice of design in relation to the ML model performances. A study is conducted that considers 12 experimental designs, seven families of predictive models, seven test functions that emulate physical processes, and eight noise settings, both homoscedastic and heteroscedastic. The results of the research can have an immediate impact on the work of practitioners, providing guidelines for practical applications of DOE and ML.

Design choice and machine learning model performances