RSFs operate by constructing multiple decision trees during the training phase. Each tree is built using a random subset of the data, and the survival time is predicted by aggregating the results from these trees. The main difference from traditional random forests lies in the splitting criteria used to build the trees. In RSFs, the trees are split based on survival data, often using measures like the log-rank test to maximize the difference in survival between groups.
