Ensemble Learning

Ensemble Learning refers to the process of building multiple models and then combining them in a way that can produce better results than individual models. These individual models can be classifiers, regressors, or anything else that models data in some way. Ensemble learning is used extensively across multiple fields including data classification, predictive modeling, anomaly detection, and so on.

Why ensemble learning: In order to understand this, let's take a real-life example. You want to buy a new TV, but you don't know what the latest models are. Your goal is to get the best value for your money, but you don't have enough knowledge on this topic to make an informed decision. When you have to make a decision about something like this, you go around and try to get the opinions of multiple experts in the domain. This will help you make the best decision. More often than not, instead of just relying on a single opinion, you tend to make a final decision by combining the individual decisions of those experts. The reason we do that is because we want to minimize the possibility of a wrong or suboptimal decision.