This video introduces bagging and boosting, two ensemble learning methods that combine multiple models to improve performance, with an overview of how they work, including their differences in model application order and computational requirements.
This video is a part of the L4-ML Machine Learning: Specialization self-paced course. To learn more about it and enroll into the course, visit https://www.knime.com/learning
