say I have a sensor that measures temperature, pressure ++, and want to use this data to predict some quantity "A". If I use multivariate regression, I can simply implement a model of the form A=a0+a1x1+a2x2+..., and whenever I have new measurements I can use the model to make predictions.
If I on the other hand make a predictive model using random forests, I'm not really sure how to use it. I've used the caret package to split my data into training and test sets, and do automatic feature selection using random forest and cross-validation. I get good predictions on the test set, but have no idea how to implement these trees to use in say a digital signal processor. In R I just use the predict() function, but this is obviously not available outside of R.
This is probably a stupid questing, but it's the best I can do.
Any suggestions are welcome.