  Due to their high predictive performance and flexibility, machine learning
models are an appropriate and efficient tool for ecologists. However,
implementing a machine learning model is not yet a trivial task and may seem
intimidating to ecologists with no previous experience in this area. Here we
provide a series of tips to help ecologists in implementing machine learning
models. We focus on classification problems as many ecological studies aim to
assign data into predefined classes such as ecological states or biological
entities. Each of the nine tips identifies a common error, trap or challenge in
developing machine learning models and provides recommendations to facilitate
their use in ecological studies.
