A simple majority classifier is one where every point is assigned to whichever class is in the majority in the training set. (If there is no majority, one of the classes is chosen arbitrarily). This classifier is often used as a baseline for comparing other machine learning techniques.

What is majority voting in machine learning?

A voting ensemble (or a “majority voting ensemble“) is an ensemble machine learning model that combines the predictions from multiple other models. It is a technique that may be used to improve model performance, ideally achieving better performance than any single model used in the ensemble.

What is a classifier and what is its purpose?

A classifier is a hypothesis or discrete-valued function that is used to assign (categorical) class labels to particular data points. In the email classification example, this classifier could be a hypothesis for labeling emails as spam or non-spam.