Random Forest (Ensembling Technique)

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Mastering Ensemble Learning: Bagging & Boosting Explained!

Welcome to today's workshop on Ensemble Learning! 🚀 In this session, we will explore how combining multiple models improves accuracy and performance.

📌 Timestamps:

⏳ 0:00 - Introduction to Ensemble Learning
⏳ 0:20 - What is Ensemble Learning?
⏳ 0:42 - Two Main Techniques: Bagging & Boosting
⏳ 0:52 - What is Bagging (Bootstrap Aggregation)?
⏳ 1:10 - How Bagging Works: Training Models in Parallel
⏳ 1:36 - Why Bagging Helps Reduce Variance & Overfitting
⏳ 2:00 - Random Forest: How It Uses Bagging
⏳ 3:00 - How Random Forest Works (Step-by-Step Explanation)
⏳ 5:00 - Example: Decision Trees & Majority Voting in Random Forest
⏳ 7:00 - Boosting Explained: Training Models Sequentially
⏳ 8:00 - How Boosting Corrects Previous Model Mistakes
⏳ 9:00 - Examples of Boosting: XGBoost, AdaBoost
⏳ 10:00 - Comparing Bagging vs. Boosting
⏳ 11:00 - Summary & Final Thoughts

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