Bagging | Introduction | Part 1

Bagging, or Bootstrap Aggregating, is an ensemble method that involves training multiple models independently on different subsets of the training data. These models are then combined through averaging or voting to make predictions. Bagging reduces variance and improves the stability and accuracy of the final model, making it a popular choice in machine learning. Code used: https://github.com/campusx-official/bagging-ensemble Bias Variance Tradeoff: https://www.youtube.com/watch?v=74DU02Fyrhk ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 00:25 - Plan of attack 01:50 - What is Bagging 14:32 - Code Demo 30:50 - Outro