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What are some useful guidelines for testing parameters (i.e. interaction depth, minchild, sample rate, etc.) using GBM?

Let's say I have 70-100 features, a population of 200,000 and I intend to test interaction depth of 3 and 4. Clearly I need to do some testing to see what combination of parameters holds up best out-of-sample. Any suggestions on how to approach this test design?

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The caret package can help you optimize the parameter choice for your problem. The caretTrain vignette shows how to tune the gbm parameters using 10-fold repeated cross-validation - other optimization approaches are available it can all run in parallel using the foreach package. Use vignette("caretTrain", package="caret") to read the document.

The package supports tuning shrinkage, n.trees, and interaction.depth parameters for the gbm model, though you can add your own.

For heuristics, this is my initial approach:

shrinkage: As small as you have time for (the gbm manual has more on this, but in general you can nver go wrong with a smaller value). Your data set is small so I'd probably start with 1e-3

n.trees: I usually grow an initial model adding more and more trees until gbm.perf says I have enough (actually, typically to 1.2 times that value) and then use that as a guide for further analysis.

interaction.depth: you already have an idea about this. Try smaller values as well. Maximum value is floor(sqrt(NCOL(data)).

n.minobsinnode: I find it really important to tune this variable. You don't want it so small that the algorithm finds too many spurious features.

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