Techniques to improve ecological interpretability of black-box machine learning models.

ABSTRACT: Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological h

SUBMITTER: Welchowski T 

PROVIDER: S-EPMC10442734 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature