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    Author(s): D. Richard Cutler; Thomas C. Edwards; Karin H. Beard; Adele Cutler; Kyle T. Hess; Jacob Gibson; Joshua J. Lawler
    Date: 2007
    Source: Ecology. 88(11): 2783-2792.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (189.0 KB)


    Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

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    Cutler, D. Richard; Edwards, Thomas C.; Beard, Karin H.; Cutler, Adele; Hess, Kyle T.; Gibson, Jacob; Lawler, Joshua J. 2007. Random forests for classification in ecology. Ecology. 88(11): 2783-2792.


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    additive logistic regression, classification trees, LDA, logistic regression, machine learning, partial dependence plots, random forests, species distribution models

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