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: github.com/campusx-official/b...
Bias Variance Tradeoff: • Bias Variance Trade-of...
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⌚Time Stamps⌚
00:00 - Intro
00:25 - Plan of attack
01:50 - What is Bagging
14:32 - Code Demo
30:50 - Outro