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I'm using the random forests algorithm in species distribution context. I have presence-absence as a dependent variable, so I decided to work with classification. At this point, I recovered the probability of presence this way :

predict(RF_MODEL, test_, type='prob')[,2]

If I want to compare the results of this model with other models "glm", "gam" and "brt", can we do this or we have to work with regression to have the estimation of occurrence probabilities as the other models?

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    Well, you have to evaluate different measures including model fit, performance as well as estimated spatial uncertainty. In Random Forests the fit is indicated by the internal Bootstrap validation and is in the reported statistics. However, performance is an evaluation of prediction against data not in the model. You can do this via a cross-validation withhold or an approach like rfUtilities::rf.crossValidation which produces an error distribution based on a permutation. The spatial uncertainty can be evaluated using an infinitesimal Jackknife or U-statistic of the estimate variance. – Jeffrey Evans Jan 25 at 18:30
  • @JeffreyEvans, thank you very much for your very interesting explanations. I want to know if I can consider the probability estimated by RF above as occurrence probability as the for example the one extracted by logistic regression for example? – user2300 Jan 27 at 0:04

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