Hyperparameter Tuning for Machine and Deep Learning with R

Eva Bartz is an expert in law and data protection. Within the wide area of data protection, she specializes particularly in the application of artificial intelligence and its benefits and dangers. Based on this vast experience, she founded Bartz & Bartz GmbH in 2014 together with Thomas Bartz-Beielstein and offers consulting for a variety of customers. She translates the academic expertise of Bartz & Bartz GmbH’s advisors - who are leading experts in their fields - into a benefit for her customers. One of these customers was the Federal Statistical Office of Germany (Destatis), and the study for them laid the groundwork for this book. Prof. Dr. Thomas Bartz-Beielstein is an artificial intelligence expert with 30+ years of experience. He is a professor of applied mathematics at TH Köln in Germany and the director of the Institute for Data Science, Engineering, and Analytics (IDE+A). His research lies in artificial intelligence, machine learning, simulation, and optimization. Hedeveloped the Sequential Parameter Optimization (SPO). SPO integrates approaches from surrogate model-based optimization and evolutionary computing. He has worked on diverse topics from applied mathematics and statistics, design of experiments, simulation-based optimization and applications in domains as water industry, elevator control, or mechanical engineering.Prof. Dr. Martin Zaefferer is a professor at Duale Hochschule Baden-Württemberg Ravensburg, teaching subjects related to data science in business informatics. Previously, he worked as a consultant at Bartz & Bartz GmbH and as a researcher at TH Köln, where he also studied electrical engineering and automation. He received a PhD from the Department of Computer Science at TU Dortmund University. Subsequently, he developed a keen interest in researching methods from the intersection of optimization and machine learning algorithms. He is passionate about the analysis of complex processes and finding novel solutions to challenging real-world problems.Prof. Dr. Olaf Mersmann is a professor of data science at TH Köln-University of Applied Sciences in Germany and a member of the Institute for Data Science, Engineering, and Analytics (IDE+A). Having studied physics, statistics and data science, his research interests include landscape analysis for black box optimization problems and industrial machine learning applications. He is one of the developers of the exploratory landscape analysis approach to characterize continuous function landscapes.

• Chapter 1: Introduction.- Chapter 2: Tuning.- Chapter 3: Models.- Hyperparameter Tuning Approaches.- Chapter 5: Result Aggregation.- Chapter 6: Relevance of Tuning in Industrial Applications.- Chapter 7: Hyperparameter Tuning in German Official Statistics.- Chapter 8: Case Study I.- Chapter 9: Case Study II.- Chapter 10: Case Study III.- Chapter IV: Case Study IV.- Chapter 12: Global Study.