Part 10/11:
Learning from Mistakes: Boosting relies on previous models' mistakes to inform new iterations, while models in bagging function independently of each other.
Strength of Models: Boosting effectively transforms weak models into strong learners, contrasting with bagging, which operates with robust models to minimize variability.
Overfitting Risk: Boosting can be more susceptible to overfitting if not carefully managed, compared to bagging, which typically has lower risks due to its parallel structure.