Nine tips for ecologists using machine learning
This is an incremental guide for ecologists with no prior experience in machine learning to improve model implementation in their studies.
The paper addresses the challenge for ecologists in implementing machine learning models by providing nine tips to avoid common errors and facilitate their use in ecological classification problems.
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.