Artificial Intelligence Programming Practice Exam

Boosting provides a significant advantage by focusing on training weaker models iteratively to create a strong overall model. In boosting, each subsequent model is trained to correct the errors made by the previous models. This iterative process allows boosting to refine its predictions progressively, weighting instances differently based on their difficulty in being classified correctly. As a result, boosting emphasizes the strength of previously performed weak learners and effectively enhances the overall predictive power of the ensemble.

This method contrasts with bagging, which builds multiple models independently and combines their predictions to reduce variance. Bagging does not inherently focus on correcting the mistakes of previous models, thus leading to different strengths in handling bias and variance. By using weak models iteratively, boosting harnesses the ability to create a highly accurate model that combines multiple iterations of learning, resulting in enhanced performance on complex datasets.