SDMtune : An R package to tune and evaluate species distribution models
| Autor: | Arnaud G. Barras, Raphaël Arlettaz, Veronika Braunisch, Sergio Vignali |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Feature selection Overfitting computer.software_genre 010603 evolutionary biology 01 natural sciences 03 medical and health sciences Genetic algorithm genetic algorithm ecological niche model QH540-549.5 Ecology Evolution Behavior and Systematics Original Research 030304 developmental biology Nature and Landscape Conservation Hyperparameter 0303 health sciences Ecology Artificial neural network Principle of maximum entropy model complexity Random forest machine learning fine‐tuning 570 Life sciences biology Data mining Akaike information criterion computer variable selection |
| Zdroj: | Ecology and Evolution, Vol 10, Iss 20, Pp 11488-11506 (2020) Ecology and Evolution Vignali, Sergio; Barras, Arnaud Gian; Arlettaz, Raphaël; Braunisch, Veronika (2020). SDMtune: An R package to tune and evaluate species distribution models. Ecology and evolution, 10(20), pp. 11488-11506. John Wiley & Sons, Inc. 10.1002/ece3.6786 |
| ISSN: | 2045-7758 |
| DOI: | 10.1002/ece3.6786 |
| Popis: | Balancing model complexity is a key challenge of modern computational ecology, particularly so since the spread of machine learning algorithms. Species distribution models are often implemented using a wide variety of machine learning algorithms that can be fine‐tuned to achieve the best model prediction while avoiding overfitting. We have released SDMtune, a new R package that aims to facilitate training, tuning, and evaluation of species distribution models in a unified framework. The main innovations of this package are its functions to perform data‐driven variable selection, and a novel genetic algorithm to tune model hyperparameters. Real‐time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance. SDMtune supports three different metrics to evaluate model performance: the area under the receiver operating characteristic curve, the true skill statistic, and Akaike's information criterion corrected for small sample sizes. It implements four statistical methods: artificial neural networks, boosted regression trees, maximum entropy modeling, and random forest. Moreover, it includes functions to display the outputs and create a final report. SDMtune therefore represents a new, unified and user‐friendly framework for the still‐growing field of species distribution modeling. The main innovations of SDMtune are a novel genetic algorithm to tune the hyperparameters of a model and functions to perform data‐driven variable selection. Real‐time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance. |
| Databáze: | OpenAIRE |
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