Majority voting is the simplest ensemble learning technique that allows the combination of multiple base learner's predictions. Similar to how elections work, the algorithm assumes that each base learner is a voter and each class is a contender. The algorithm takes votes into consideration in order to elect a contender as the winner. There are two main approaches to combining multiple predictions with voting: one is hard voting and the other is soft voting. We present both approaches here.
Hard and soft voting
Hard voting
Hard voting combines a number of predictions by assuming that the most voted class is the winner. In a simple case of two classes and three base learners, if a target class has at least two votes, it...
