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Definition

Bayesian Optimization

Bayesian Optimization is a sequential design strategy for global optimization of black-box functions. It is widely used in machine learning to tune hyperparameters, particularly when evaluating the target function is computationally expensive.

Frequently Asked Questions

How does Bayesian optimization work?

It builds a probabilistic model (usually a Gaussian Process) of the objective function and uses an acquisition function to decide where to sample next, balancing exploration and exploitation.

Why is Bayesian optimization preferred over grid search?

Because it keeps track of past evaluation results to make informed decisions about the next hyperparameter set to test, finding optimal values in far fewer iterations.

Quick Facts

  • CategoryMathematical Foundations
  • Key ApplicationHyperparameter tuning, automated machine learning (AutoML), and experimental design.

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