Description: Optimization criteria are standards used to evaluate the quality of solutions in various technological contexts. These criteria allow measuring the effectiveness and efficiency of algorithms and models, ensuring optimal results in specific tasks. In the realm of machine learning, optimization criteria are fundamental to ensure that models trained on diverse datasets maintain adequate performance without compromising data privacy. In model optimization, these criteria help adjust parameters and select features that enhance the model’s accuracy and generalization. In the context of automated machine learning (AutoML), optimization criteria are essential for automating the model selection and hyperparameter tuning process, enabling users to obtain high-quality models without manual intervention. Finally, in evaluation, optimization criteria are used to compare different models and approaches, facilitating the identification of the best solution for a given problem. In summary, optimization criteria are key tools in data science and machine learning, providing a framework for continuous improvement and informed decision-making.
