Majority voting is a simple ensemble technique used in machine learning to combine the predictions of multiple individual models in order to make a final prediction. This technique is particularly common in classification tasks, where the goal is to assign a class label to an input data point.
Majority voting is effective because it can help mitigate the weaknesses of individual models. While one model might make errors on certain types of data, another model might perform better on those cases. By combining their predictions, the ensemble can potentially provide more accurate and robust results than any single model. Majority voting in a simple technique, and can be superseded by more-powerful techniques such as weighted voting, stacking, and boosting.
Here's how majority voting works…
Individual Models: First, you train multiple individual models on the same dataset using possibly different algorithms, hyperparameters, or subsets of the data. Each model produces its own predictions for the input data.
Voting: When a new data point needs to be classified, you pass it through all the individual models, and each model makes a prediction based on its learned rules. These predictions are then aggregated.
Majority Decision: The final prediction is determined by selecting the class label that receives the most votes from the individual models. In case of a tie, you can either choose one of the tied classes arbitrarily or use a predefined strategy to break the tie.