What is voting in ensemble learning and what are its types?

Voting combines predictions from multiple models using majority or weighted decision rules.

Voting ensembles aggregate predictions from multiple models. Hard voting selects the most frequent class label among models. Soft voting averages predicted probabilities and selects the class with the highest probability. Weighted voting assigns different importance to models based on performance. Soft voting generally performs better when models are well-calibrated.