What is ensemble learning?

Ensemble learning involves building multiple models and then combining them in such a way that it produces better results than what the models could produce individually. These individual models can be classifiers, regressors, or other models.

Ensemble learning is used extensively across multiple fields, including data classification, predictive modeling, and anomaly detection.

So why use ensemble learning? In order to gain understanding, let's use 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 must decide about something like this, you might get the opinions of multiple experts in the domain. This will help you make the best decision. Often, instead of relying on one opinion, you can decide by combining the individual decisions of those experts. Doing...