Ensemble Learning in ML: Boost Your Model's Performance (Bagging, Boosting, Stacking!)

Welcome to your comprehensive guide on Ensemble Learning in Machine Learning! 🚀 Discover one of the most powerful techniques to significantly boost your model's performance by combining multiple "weak" models into one strong, robust predictor.

This video will explain why ensemble methods work (the "wisdom of crowds" principle), and dive deep into the three main categories: Bagging (like Random Forest), Boosting (like XGBoost), and Stacking. Learn their advantages, limitations, and when to apply them for better accuracy and generalization in your ML projects!

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