No single model is perfect. Bagging (Random Forests) reduces variance; Boosting (XGBoost) reduces bias. Together they dominate tabular ML competitions.
Learning Objectives
→Explain bootstrap sampling
→Implement a bagging classifier conceptually
→Understand boosting as sequential error correction
→Interpret feature importances from a forest
Practice
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