Before revision: It is notoriously hard to build a bad Random Forest (RF). Concurrently, RF is perhaps the only standard ML algorithm that blatantly overfits in-sample without any consequence out-of-sample.
After revision: It is notoriously difficult to build a bad Random Forest (RF). Concurrently, RF is perhaps the only standard ML algorithm that blatantly overfits in-sample without any consequence out-of-sample.