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 $1$ ...