Machine learning of poorly predictable ecological data

This paper reports on research using a variety of machine learning techniques to a difficult modelling problem, the spatial distribution of an endangered Australian marsupial, the southern brown bandicoot (Isoodon obesulus). Four learning techniques – decision trees/rules, neural networks, support vector machines and genetic programming – were applied to the problem. Support vector and neural network approaches gave marginally better predictivity, but in the context of low overall accuracy, decision trees and genetic programming gave more useful results because of the human comprehensibility of their models.