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Related Questions
- What are the key differences between random search and Bayesian optimization in hyperparameter tuning?
- How can we efficiently explore high-dimensional spaces in random search to find optimal hyperparameters?
- What are some common heuristics used in random search to reduce the dimensionality of the search space?
- Can you explain the concept of acquisition functions in Bayesian optimization and their role in selecting the next point to evaluate?
- How can we balance exploration and exploitation in Bayesian optimization to avoid getting stuck in local optima?
- What are some popular Bayesian optimization libraries in Python that support high-dimensional hyperparameter tuning?
- In what scenarios is random search preferred over Bayesian optimization, and vice versa, in hyperparameter tuning?
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