Description: Sequential model optimization is a strategy that seeks to improve the performance of predictive models by optimizing hyperparameters. This technique is based on building a model that represents the objective function, that is, the relationship between hyperparameters and model performance. Through this approach, different combinations of hyperparameters can be explored more efficiently than with traditional methods, such as random search or grid search. Sequential model optimization uses algorithms like Bayesian optimization, which allow for inferences about the objective function and select the most promising hyperparameters at each iteration. This iterative process not only saves time but can also lead to better results by avoiding exhaustive evaluation of all possible combinations. The relevance of this technique lies in its ability to handle complex and costly search spaces, which is especially useful in the context of machine learning models where fine-tuning hyperparameters can make a significant difference in the final model performance. In summary, sequential model optimization is a powerful tool for enhancing the effectiveness of predictive models by enabling a more intelligent and targeted search in the hyperparameter space.
