Hi,



I’d appreciate your thoughts regarding the following. I’m working with an
insurance data set with the objective of predicting a binary outcome (claim
or not). Policyholders in the sample are not observed for equivalent time
periods. I have an exposure variable that reflects the amount of time each
individual has been observed. In traditional GLM models, the usual way to
handle this is to use the exposure as an offset variable (i.e. the coefficient
for this variable is fixed at 1).



I would like to extend the class of models I can use to model this data,
using more recent techniques such as Support Vector Machines, Neural Nets,
etc. But my question is how I can include the exposure information in the
model in the way I do with GLM.  I’m especially interested in SVM in R.



Any thoughts are much appreciated.


Regards,

Lars.

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