I am building species distribution models with boosted regression trees and support vector machines using a large number of Presence-(Pseudo)absence data (> 10.000 plots) Since my goal is not to predict occurences but rather probabilities of occurence, I would like to use support vector regression rather than classification.
However, even though the results look promising so far, I am not sure if treating the binary Y as a continuous variable makes sense statistically. I am familiar using SVM for classifiation (e.g. of remote sensing data) but have never used the ε-regression method before. I am using the e1071-package in R with an RBF-kernel, 19 predictors in total.
From how I see it, the application should be relatively simple, since like SVM classification, SVR works by transforming the data into a feature space, then performing linear regression in that vector space with the linear ε-insensitive loss function ignoring errors that are within the ε distance of the observed value by treating them as zero. After considering the scaling of predictions as well as the prevalence of presence-data, I believe it should be possible to interpret results as occurence probabilities. However, since I'm not coming from a data mining - background, I feel like it's easy to miss some basic concept of SVR that I haven't understood so far. In fact, I haven't found any literature about SVR treating a binary variable as a continuous. Any advise or hints on that matter would be highly appreciated. Thank you