Ensemble models in machine learning algorithms are collections of multiple individual models that work together to produce a single prediction. Each model is trained on the same dataset with different parameter settings or different input features or both, and the predicted outputs from these models are combined using an aggregate function such as the mean or median of their predictions. This aggregation produces a more robust output than any single model could provide alone. Ensemble models are often used for high$-$precision applications such as image recognition, speech recognition, and financial forecasting.

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