This choice of objective R Θ is motivated by Ng (2004), who suggests that, given a learning setting where the number of irrelevant features is exponential in the number of training examples, we can nonetheless learn effectively by building decision trees to minimize the 1regularized log-loss.
(This convention was established by Magerman (1995).)