In both bagging and random forests a set of trees is built on random samples of the learning sample: In each step of the algorithm, either a bootstrap sample (of the same size, drawn with replacement) or a subsample (of smaller size, drawn without replacement) of the learning sample is drawn randomly, and an individual tree is grown on each sample. As outlined above, each random sample reflects the same data generating process but differs slightly from the original learning sample due to random variation. Keeping in mind that each individual classification tree depends highly on the learning sample as outlined above, the resulting trees can thus differ substantially.
