Types of Ensemble Learning

Majority voting

Majority voting is a simple and widely used technique in ensemble learning that combines the predictions of multiple individual models (often called base models or weak learners) to make a final prediction. The idea behind majority voting is straightforward: each model in the ensemble makes a prediction, and the final prediction is determined by a majority vote among these individual predictions.

Consider an example of binary classification where we aim to determine whether a test data point belongs to class $0$ or class $1$. Additionally, suppose we have three trained models (weak learners) that provide predictions for the class of that test point. Our ensemble model makes its prediction by considering the majority output, meaning if most of the models predict $0$, our ensemble model will also predict $0$. This process is illustrated in the accompanying animation.