Ensemble Machine Learning Techniques 3.2: How Bagging Works

This video tutorial introduces the concept of bagging, an ensemble technique that uses bootstrapping to create multiple sub-samples from a dataset. Models are built on these sub-samples, and their predictions are aggregated to improve accuracy. The video explains the process with a diagram and provides pseudocode for implementing bagging in Python. It concludes with a preview of the next video, which will cover using bagging with SVM for movie rating predictions.

What is the main purpose of the bagging technique in machine learning?

Describe the process of creating sub-samples using bootstrapping in bagging.

How do models built on sub-samples contribute to the final prediction in bagging?

What are the two methods mentioned for combining predictions in bagging?

Explain the steps involved in implementing bagging in Python as described in the video.