multi-model ensembles

Multi-model ensembles combine predictions from multiple different models to improve accuracy and reliability. Instead of relying on a single model, which might have specific biases or limitations, ensemble methods analyze the outputs of various models and synthesize a final result. This approach leverages the strengths and offsets the weaknesses of individual models, leading to more robust and accurate predictions. It's similar to consulting multiple experts to reach a well-informed decision. In fields like weather forecasting or machine learning, ensembles help reduce errors and increase confidence in the results.