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Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach.

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第一作者： Inna,Kolyshkina

第一单位： Analytikk Consulting, Sydney, NSW, Australia.

作者： Inna,Kolyshkina ^[1] ; Simeon,Simoff ^[2]

作者单位： Analytikk Consulting, Sydney, NSW, Australia. ^[1] School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia.;MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW, Australia. ^[2]

关键词 CRISP-ML cross-industry standard process data science methodology interpretability interpretability matrix machine learning necessary level of interpretability public health

DOI 10.3389/fdata.2021.660206

PMID 34124652

发布时间 2023-09-20

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