Description: The ‘Expected Improvement’ is a fundamental concept in Bayesian optimization, used to guide the sampling process in the search for the best hyperparameters for machine learning models. This criterion is based on the idea that by evaluating different hyperparameter configurations, one can estimate the expected gain that would be obtained by selecting a particular option. Essentially, expected improvement measures how much better the model’s performance is expected to be if a specific set of hyperparameters is chosen compared to the best performance observed so far. This approach allows researchers and developers to make informed decisions about where to conduct new evaluations, thus maximizing the efficiency of the optimization process. Expected improvement is calculated by considering both the mean and variance of the objective function, allowing for a balance between exploring new areas of the hyperparameter space and exploiting areas that have already proven to be promising. This balance is crucial, as excessive exploration can lead to inefficient resource use, while excessive exploitation can result in missing opportunities to find even better configurations. In summary, expected improvement is a powerful tool that helps optimize the performance of complex models through a systematic and probability-based approach.
