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Ensemble (Boosting, Bagging, and Stacking) in Machine Learning: Easy Explanation for Data Scientists

Questions about Ensemble Methods frequently appear in data science interviews. In this video, I’ll go over various examples of ensemble learning, the advantages of boosting and bagging, how to explain stacking, and more! 🟢Get all my free data science interview resources https://www.emmading.com/resources 🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case... 🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-i... 🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-i... 🔵 Data Science Resume Checklist https://www.emmading.com/data-science... ✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching // Comment Got any questions? Something to add? Write a comment below to chat. // Let's connect on LinkedIn:   / emmading001   ==================== Contents of this video: ==================== 00:00 Introduction 00:38 Ensemble Methods 01:40 Bagging (Bootstrap Aggregation) 03:00 Example: Random Forest 03:44 Boosting 05:14 Example: Gradient-Boosted Trees 05:47 Bagging vs. Boosting 06:40 Stacking 07:08 Two-Level Ensemble 07:44 Pros and Cons