How does Ensemble Learning work?

Ensemble Learning is a technique in which the predictions or results of multiple models are combined to achieve better performance. Let’s take an example of if you buy a car, you generally go for research on the web to search for reviews and features of different cars. In the end, after combining all the reviews, you create your own review of that car and decide whether you want to purchase it or not. The review you create is the better version of all the reviews you read because it contains the information from all the reviews.

Ensemble learning works the same: the predictions from many algorithms are used to create a better model.

Ensemble Learning can be done in two ways. One is to combine different algorithm predictions to generate a new high-accuracy prediction. Another way is to use a single algorithm multiple times and, in the end, use each model prediction to generate a better model with good accuracy.

Ensemble Learning uses several machine learning models built with different learning algorithms to improve the accuracy of the prediction. It basically averages the predictions of several models to get a better prediction.

It is like a set of models where we

  1. don’t want all models to be identical
  2. don’t want them to be different for the sake of it