All of the effects of the different variables are not necessarily found in a single tree. For example, at a given node there may be more than one significant variable on which to split, but obviously only one of these can be used as a splitter at a time. This is why multiple trees are usually interactively explored or sampled using the random tree creation menu described in this chapter.

Multiple random trees are also the source for some of the most sophisticated analysis provided by ChemTree. The observation distance matrix (covered in Chapter 12) and the correlation interaction view (covered in Chapter 13) use multiple random tree models.