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Related Questions
- What are the key factors to consider when selecting base models for an ensemble model?
- How does the diversity of base models impact the performance of an ensemble model?
- What are the benefits and drawbacks of using a large number of ensemble members versus a smaller number?
- How does the choice of ensemble method (e.g. voting, averaging, stacking) affect the performance of the ensemble model?
- Can you provide examples of successful ensemble models in different domains (e.g. image classification, natural language processing, recommender systems)?
- How does the concept of 'ensemble size' relate to the number of base models and the number of ensemble members?
- What are some common pitfalls to avoid when designing and training ensemble models, and how can they be mitigated?
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