Lec-25: BAGGING vs. BOOSTING vs STACKING in Ensemble Learning | Machine Learning
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Lec-25: BAGGING vs. BOOSTING vs STACKING in Ensemble Learning | Machine Learning
Bagging, Boosting, and Stacking are three key ensemble methods in machine learning, each designed to enhance model accuracy in different ways. In this video, we’ll break down how each technique works, their differences, and when to use them to get the best results from your models. 00:00 – Introduction 00:16 – Bagging 02:49 – Boosting ...
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