In this chapter, we consider six problems of transforming decision rule systems into decision trees. For these problems, we study algorithms describing, for given tuples of attribute values, the computational paths in decision trees obtained from decision rule systems. Our goal is to generalize the algorithms discussed in Chap. 2 to the case of weighted depth of decision trees. For each problem, we study two algorithms based on node covers of the hypergraphs corresponding to the decision rule systems and a completely greedy algorithm for describing the computation paths. We also consider a dynamic programming algorithm that, given decision rule systems and weights of attributes, returns the minimum weighted depth of decision trees for these systems.