How does Random Forest relate to stability theory in statistical learning?

Random Forest improves generalization through algorithmic stability induced by bootstrapping and feature randomness.

Stability in statistical learning measures how sensitive a model is to small perturbations in the training data. Random Forest improves stability by averaging many unstable base learners (deep decision trees). Each tree is highly sensitive to data changes, but bootstrapping ensures each tree sees a different sample, and averaging reduces sensitivity at the ensemble level. This leads to lower expected generalization error because stable algorithms generalize better under uniform stability bounds.