Voting

Voting is an ensemble method in machine learning where multiple algorithms make predictions, and the final output is determined by majority vote or weighted aggregation.

Voting is used in scenarios where combining the predictions of multiple models can improve accuracy and robustness, such as in classification tasks. Voting works by training multiple models on the same dataset and then combining their predictions. In majority voting, the class with the most votes is chosen as the final prediction. In weighted voting, each model’s prediction is weighted by its accuracy or another metric, and the final prediction is based on the weighted sum. For instance, in a binary classification problem, if three models predict the classes as [0, 1, 1], the final prediction using majority voting would be 1. If the models have weights [0.2, 0.5, 0.3], the weighted sum would be 0.2*0 + 0.5*1 + 0.3*1 = 0.8, resulting in a final prediction of 1.