OKABAYASHI, M. Aimi (2021) Prediction of sets of species by adaptive thresholding of categorical predictive models PRE - Research Project, ENSTA.



Deep species distribution models have recently shown superior predictive capabilities to classical methods used in ecology, especially when trained on very large volumes of occurrence data. One problem with these models, however, is that they can only predict the probability of a species conditional on an observation but they cannot predict species assemblages. In this study, we explore adaptive thresholding methods whose objective will be to predict more relevant assemblages than naive methods such as absolute thresholding or the k most probable species. We rely on statistical tests rejecting a null hypothesis, and a posteriori probability calibration method.

Item Type:Thesis (PRE - Research Project)
Subjects:Mathematics and Applications
ID Code:8465
Deposited By:aimi Okabayashi
Deposited On:16 mars 2022 17:12
Dernière modification:16 mars 2022 17:12

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