Model Type and Bias-Variance Tradeoff
📌 The key difference between Bagging and Boosting relates to the type of base models used and how they address the bias-variance tradeoff.
🌲 Bagging uses base models (like decision trees) that generally have low bias but high variance (e.g., deep, complex trees).
⚙️ Boosting typically employs base models that have high bias but low variance (e.g., shallow decision trees or stumps).
📊 The goal of Bagging is to reduce variance by averaging uncorrelated models, while Boosting aims to reduce bias by sequentially fitting models to the residuals of previous ones.

Learning Sequence and Dependency
⏱️ In Bagging, base models are trained in parallel; they are independent of each other, and their predictions are aggregated democratically (equal weighting).
🔄 In Boosting, models are trained sequentially; each subsequent model focuses on correcting the errors (residuals) made by the preceding model, making it highly dependent on prior performance.
📚 Bagging involves randomly sampling subsets of the data to train multiple models simultaneously.

Weight Distribution of Base Models
🗳️ In Bagging, base models are typically given equal weight in the final prediction, operating like a democracy.
🥇 In Boosting, models are assigned unequal weights based on their individual performance; better-performing models have a greater influence on the final output.

Key Points & Insights
➡️ When choosing between Bagging and Boosting, consider the base algorithm: use Bagging if your algorithm performs well on the training data but poorly on unseen data (high variance).
➡️ Use Boosting if your algorithm performs poorly on the training data but is less sensitive to changes in the test data (high bias).
➡️ The parallel nature of Bagging allows for faster training compared to the sequential dependency inherent in Boosting.

📸 Video summarized with SummaryTube.com on Nov 28, 2025, 08:32 UTC