Instruction: Discuss the rationale behind using ensemble methods and how they improve model performance.
Context: The question aims to assess the candidate's understanding of ensemble methods, including the theoretical basis for why combining multiple models often leads to better performance than individual models.
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The way I'd explain it in an interview is this: Model ensembling means combining multiple models so the final prediction is usually more stable or more accurate than any single model alone. The benefit comes from the fact that different models...