Validation and Evaluation techniques for Data Driven models

This nugget on “Validation and Evaluation Techniques for Data-Driven Models” explores essential methods for assessing the accuracy, reliability, and performance of data-driven models. It covers four key areas: model-to-model validation, historical data validation, parameter tuning, and sensitivity analysis. Participants will gain insights into comparing data-driven models with physical or simulated systems, validating model accuracy using historical data, optimizing model performance through parameter tuning, and testing model sensitivity to input and parameter changes. The course emphasizes the importance of validation and evaluation in ensuring trustworthy and effective data-driven models. Participants will learn practical techniques, best practices, and case studies to enhance their understanding of these validation and evaluation methods. By the end of the course, participants will have a solid foundation in validating and evaluating data-driven models, enabling them to make informed decisions and maximize the benefits of these models in their respective fields.