Moritz S. Schmid, PhD
Underwater imaging, deep learning, and plankton ecology at Oregon State University
Use of Machine Learning for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook
Huettmann F, Craig EH, Herrick KA, Baltensperger AP, Humphries GRW, Lieske DJ, Miller K, Mullet TC, Oppel S, Resendiz C, Rutzen I, Schmid MS, Suwal MK, Young BD (2018) Use of Machine Learning (ML) for Predicting and Analyzing Ecological and “Presence Only” Data: An Overview of Applications and a Good Outlook. In: Machine Learning for Ecology and Sustainable Natural Resource Management, 2nd edn. Springer International Publishing, Cham, p 27–61
Publication year: 2018
2.1 Introduction
Over a decade ago, Leo Breiman (2001a) wrote: “There are two cultures in the use
of statistical modeling to reach conclusions from data. One assumes that the data
are generated by a given stochastic data model. The other uses algorithmic models
and treats the data mechanism as unknown. The statistical community has been
committed to the almost exclusive use of data models. This commitment has led to
irrelevant theory, questionable conclusions, and has kept statisticians from working
on a large range of interesting current problems. Algorithmic modeling, both in
theory and practice, has developed rapidly in fields outside statistics.”
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