/home /blog 05 Mar 2016 |

The Machine Learning behind Voting.

Deciding who comes to power in a Democracy is a big decision. On it rests the future of the nation. The people had better not get this decision wrong. So how do we go about ensuring that our decisions are good? Voting is the answer almost everyone came up with around the world. Is there a reason it works?

Sure there is. It's called ensemble methods. What this area of Machine Learning talks about is the question "Can we have a lot of weak classifiers make a strong one?". Can we have a lot of not-so-good decision makers and somehow derive a good decision from them?

The answer to that was voting. If you have a lot of people who must vote on a certain yes/no decision, it does not matter how good they are at making that decision (as long as they are slightly better than a random decision), a good decision will be made.

Since every voter sees only a few qualities of the candidate, they make decisions based on what they see in him/her. That creates a RandomForest like situation. As we know Random Forests apply well to almost any given data set.