Combine many imperfect decision trees into one powerful ensemble. Watch how accuracy and smoothness improve as you add more trees, and compare any single tree to the full forest.
Each tree trains on a random bootstrap sample, and at every split it considers only a random subset of features (the “mtry” trick), so trees make different errors. When their votes are combined, individual errors cancel out, leaving a smoother, more accurate boundary.