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Related Questions
- What are the key factors to consider when selecting a surrogate model for Bayesian optimization in hyperparameter tuning?
- How does the choice of surrogate model affect the trade-off between exploration and exploitation in Bayesian optimization?
- Can you provide examples of popular surrogate models used in Bayesian optimization for hyperparameter tuning, and their strengths and weaknesses?
- How does the number of training samples impact the performance of different surrogate models in Bayesian optimization?
- What are the implications of using a Gaussian process as a surrogate model in Bayesian optimization, compared to other models such as random forests or neural networks?
- How can the choice of surrogate model affect the convergence rate of Bayesian optimization, and what strategies can be employed to improve convergence?
- What are some common pitfalls to avoid when selecting a surrogate model for Bayesian optimization, and how can these issues be addressed?
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