Part 9/11:
Comparing Bagging and Boosting
While bagging and boosting share the overarching goal of improving accuracy through collaboration among models, they differ significantly in methodology:
Model Combination: Boosting combines diverse models to enhance predictions, while bagging employs multiple iterations of the same model.
Focus on Bias vs. Variance: Boosting aims to reduce bias errors stemming from incorrect assumptions, whereas bagging focuses on decreasing variance errors linked to data changes.
Model Importance: In boosting, models are weighted based on performance, whereas all models in bagging carry equal weight.