5 votes
Accepted

What is a "surrogate model"?

What is Bayesian optimization? Introduction Bayesian optimization (BO) is an optimization technique used to model an unknown (usually continuous) function $f: \mathbb{R}^d \rightarrow Y$, where ...
  • 35k
5 votes

What is a "surrogate model"?

A surrogate model is a simplified model. It is a mapping $y_S=f_S(x)$ that approximates the original model $y=f(x)$, in a given domain, reasonably well. Source: Engineering Design via Surrogate ...
2 votes
Accepted

How can I interpret the value returned by score(X) method of sklearn.neighbors.KernelDensity?

The KernelDensity model learns a probability distribution from the training data. The score reflects how likely it is that any given sample has been drawn from the ...
  • 741
2 votes

Bayesian hyperparameter optimization, is it worth it?

Efficiently integrating HPO frameworks into an existing project is non-trivial. Most common datasets/tasks already have established architectures/hyperparameters/etc. and require only a few additional ...
  • 196
1 vote
Accepted

Are there Python packages for recent Bayesian optimization methods?

Apart from the Scikit-Optimize package related to Scikit-Learn, following are some of the packages related to Bayesian optimization: GPyOpt pyGPGO Hyperopt bayesian-optimization safeopt RoBO

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