Random forest
Also: random forests, random decision forest
Bagging plus feature randomness: each tree sees a bootstrap resample of the rows and considers only a random subset of features at each split, which decorrelates the trees. Averaging many deep, overfit trees keeps their low bias while cutting variance.
Example
Each bootstrap sample leaves out about 37% of the rows, so a forest of 200 trees can score every row using only the trees that never saw it, the out-of-bag estimate, with no separate validation split.