Ensemble methods: Bagging & Boosting

Bagging and boosting are commonly used terms by various data enthusiasts around the world. But what exactly bagging and boosting mean and how does it help the data science world. From this post you will learn about bagging, boosting and how they are used.

Both Bagging and boosting are part of a series of statistical techniques called ensemble methods.

Introduction to Ensemble Learning

Let’s understand the concept of ensemble learning with an example. Suppose you are a story writer and you wrote a story on some interesting topic. Now, you want to take preliminary feedback (reviews) on the story before posting it online. What are the possible ways by which you can do that?

A: You may ask one of your friends to rate the story for you.
Now it’s entirely possible that the person you have chosen loves you very much and doesn’t want to break your heart by providing a bad review to the horrible story you have written.

B: Another way could be by asking 5 colleagues of yours to give reviews to your story.
This should provide a better understanding of the story. This method may provide honest reviews for your story. But a problem still exists. These 5 people may not be “Subject Matter Experts” on the topic of your story. Sure, they might understand the essence, but at the same time may not be the best judges.