Mini toolsDefinition
A probabilistic approach to hyperparameter optimization that models the objective function using Gaussian processes.
Detailed Explanation
Bayesian optimization builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next. It maintains a balance between exploration (trying new regions) and exploitation (focusing on regions known to give good results) through acquisition functions.
Use Cases
Hyperparameter tuning, Experimental design, Automated machine learning
