Bagging Introduction Part 1

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=LUiBOAy7x6Y

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/b... • Bias Variance Tradeoff: • Bias Variance Trade-off | Overfitting... • ============================ • Do you want to learn from me? • Check my affordable mentorship program at : https://learnwith.campusx.in/s/store • ============================ • 📱 Grow with us: • CampusX' LinkedIn: / campusx-official • CampusX on Instagram for daily tips: / campusx.official • My LinkedIn: / nitish-singh-03412789 • Discord: / discord • 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

#############################

New on site