"Multiple models" can refer to several concepts across different fields, such as statistics, machine learning, simulation, and modeling. Here are a few interpretations: 1. **Statistics and Machine Learning**: In this context, multiple models refer to using more than one statistical or machine learning model to analyze data or make predictions. This can involve techniques such as ensemble learning (e.g., Random Forests, Boosting) where multiple models are combined to improve accuracy, robustness, and generalization of predictions.
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