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I know this might sound like a newbie question, but bear with me.

I have read a paper where researchers use a random forest to predict species distribution, but in their study, they only predict a new set of points given an old set and a map of environmental variables. https://www.uam.es/proyectosinv/Mclim/pdf/MBenito_EcoMod.pdf

I would like to replicate these results, only using a random forest to predict a new set of vectors given a set of vectors (the seal migrations) and some environmental variables in map form (a scalar field).

My plan would be to prepare the training data as each vector paired with the environmental variables (ocean surface temperature, ocean currents, etc.) at the head and tail of that vector.

Would a random forest be able to, given this type of data, predict a new set of vectors if I feed it a new map of environmental variables? Is this even the right way to go about this?

Thanks so much, I really appreciate anyone's feedback and or criticism.

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    $\begingroup$ The only way to find out if a particular ML method will be effective at predicting something is to gather a data set and try it (e.g., using cross-validation to evaluate the method's accuracy). So, I suspect your real question is probably something else, such as "How should I use ML techniques to predict X given Y,Z,..?" Does that sound right? $\endgroup$ – D.W. Oct 1 '15 at 18:29
  • $\begingroup$ Yeah, more than anything I'm wondering how I could use a random forest to predict a new migration pattern. Do you think the way I described could work? $\endgroup$ – jeshaitan Oct 1 '15 at 20:30
  • $\begingroup$ I've marked this one as a duplicate of your newer question, since they both seem to be asking approximately the same thing, and I'm assuming that the newer one is the one that most closely reflects what you actually want to know. Please feel free to edit the newer one to incorporate any missing information into it. $\endgroup$ – D.W. Oct 8 '15 at 20:22