What is ensemble learning?
Sometimes, one machine learning model is not good enough for a certain scenario or use case as it might not give you the desired accuracy, recall, and precision. Hence, multiple learning models—or an ensemble of models captures the pattern of the data and gives better output.
As an example, let's say we are trying to decide on a place where we would like to go in the summer. Typically, if we are planning for a trip, the suggestions for the place pours in from all corners. That is, these suggestions might come from our family, websites, friends, and travel agencies, and then we have to decide on the basis of a good experience that we had in the past:
- Family: Let's say that whenever we have consulted a family member and listened to them, there has been a 60% chance that they were proven right and we ended up having a good experience on the trip.
- Friends: Similarly, if we listen to our friends, they suggest places where we might have a good experience. In these instances...
