Introduction to Ensemble Learning

Data Science, AI and ML
#1

Let’s understand the concept of ensemble learning with an example. Suppose you are a movie director and you have created a short movie on a very important and interesting topic. Now, you want to take preliminary feedback (ratings) on the movie before making it public. What are the possible ways by which you can do that?

A: You may ask one of your friends to rate the movie 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 1-star rating to the horrible work you have created.

B: Another way could be by asking 5 colleagues of yours to rate the movie.
This should provide a better idea of the movie. This method may provide honest ratings for your movie. But a problem still exists. These 5 people may not be “Subject Matter Experts” on the topic of your movie. Sure, they might understand the cinematography, the shots, or the audio, but at the same time may not be the best judges of dark humour.

C: How about asking 50 people to rate the movie?
Some of which can be your friends, some of them can be your colleagues and some may even be total strangers.

The responses, in this case, would be more generalized and diversified since now you have people with different sets of skills. And as it turns out – this is a better approach to get honest ratings than the previous cases we saw.

With these examples, you can infer that a diverse group of people are likely to make better decisions as compared to individuals. Similar is true for a diverse set of models in comparison to single models. This diversification in Machine Learning is achieved by a technique called Ensemble Learning.