The SPM software suite includes a handy utility for changing all the variable names on your data to uninformative labels such X1, X2, etc.
Dan Steinberg's Blog
Random Forests is the unique learning machine that has no need of an explicit test sample because of its use of bootstrap sampling for every tree. This ensures that every tree in the forest is built on about 63% of the available data, leaving the remaining approximately 37% for testing [the OOB (out-of-bag) data].
If you wish to run a time series or panel data (time series cross section) style model you will frequently want to use lagged values of variables as predictors.
CART in its classification role is an excellent example of "supervised" learning: you cannot start a CART classification analysis without first selecting a target or dependent variable. All partitioning of the data into homogeneous segments is guided by the primary objective of separating the target classes. If the terminal nodes are sufficiently pure in a single target class the analysis will be considered successful even if two or more terminal nodes are very similar on most predictor variables.