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Related Questions
- How do different types of neural networks, such as linear, logistic, and decision trees, impact the accuracy and computational cost of surrogate models?
- What are the trade-offs between using a simple linear model versus a more complex model like a Gaussian process in terms of accuracy and computational cost?
- How does the choice of surrogate model, such as a random forest or a support vector machine, affect the balance between accuracy and computational cost?
- What are the implications of using a high-dimensional surrogate model versus a low-dimensional one on accuracy and computational cost?
- How do the number of training samples and the dimensionality of the input space impact the trade-off between accuracy and computational cost in surrogate models?
- What are the differences in accuracy and computational cost between using a parametric surrogate model versus a non-parametric one?
- How does the choice of optimization algorithm, such as gradient descent or quasi-Newton methods, affect the trade-off between accuracy and computational cost in surrogate models?
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