Majority Voting
Majority voting is a decision-making or classification technique where the final output is determined by the most frequent prediction among multiple models or voters. It is commonly used in ensemble learning methods, such as bagging or random forests, to combine predictions from multiple base learners to improve accuracy and robustness. This approach helps reduce variance and mitigate the impact of individual model errors by leveraging collective agreement.
Developers should learn and use majority voting when building machine learning systems that require enhanced predictive performance, especially in classification tasks where combining diverse models can lead to better generalization. It is particularly useful in scenarios with noisy data or when using weak learners, as it aggregates results to produce a more reliable outcome, such as in spam detection, medical diagnosis, or financial forecasting.