Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required.

The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

這本開放存取的書提供了豐富的實例，說明了如何在實踐中應用超參數調整，並深入探討了機器學習（ML）和深度學習（DL）方法的工作機制。本書的目的是讓讀者能夠在更短的時間、成本、努力和資源下取得更好的結果。本書中的案例研究可以在普通桌面或筆記型電腦上運行，無需高性能計算設施。

本書的構想源於Bartz＆Bartz GmbH為德國聯邦統計局（Destatis）進行的一項研究。基於該研究，本書針對工業界的從業人員以及學術界的研究人員、教師和學生。內容主要關注ML和DL算法的超參數調整，分為兩個主要部分：理論（第一部分）和應用（第二部分）。重要主題包括：重要模型參數的調查；四個參數調整研究和一個廣泛的全局參數調整研究；基於嚴重性的ML和DL方法性能的統計分析；以及一種新的、基於共識排名的方法來聚合和分析多個算法的結果。本書對六種相關的ML和DL方法的30多個超參數進行了分析，並提供了源代碼，以便用戶可以重現結果。因此，它既是一本手冊，也是一本教科書。

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. He developed 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.

Eva Bartz 是一位法律和數據保護專家。在廣泛的數據保護領域中，她專門研究人工智能的應用及其利益和危險。基於這個豐富的經驗，她於2014年與Thomas Bartz-Beielstein共同創立了Bartz & Bartz GmbH，為各種客戶提供咨詢服務。她將Bartz & Bartz GmbH的顧問的學術專業知識轉化為對客戶的利益。其中一個客戶是德國聯邦統計局（Destatis），為他們進行的研究為本書奠定了基礎。

Prof. Dr. Thomas Bartz-Beielstein 是一位擁有30多年經驗的人工智能專家。他是德國科隆應用數學教授，也是數據科學、工程和分析研究所（IDE+A）的主任。他的研究涉及人工智能、機器學習、模擬和優化。他開發了序列參數優化（SPO）方法，該方法結合了基於代理模型的優化和進化計算的方法。他在應用數學和統計學、實驗設計、基於模擬的優化以及水工業、電梯控制或機械工程等領域的應用上進行了多樣化的研究。

Prof. Dr. Martin Zaefferer 是德國巴登-符騰堡州拉文斯堡的雙軌高等專科學校的教授，教授與商業信息學相關的數據科學課程。他曾在Bartz & Bartz GmbH擔任顧問，並在德國科隆擔任研究員，同時他也在那裡學習了電氣工程和自動化。他在多特蒙德工業大學計算機科學系獲得博士學位。隨後，他對優化和機器學習算法的交叉方法進行了深入研究。他熱衷於分析複雜過程，並尋找解決具有挑戰性的現實問題的新方法。

Prof. Dr. Olaf Mersmann 是德國科隆應用科學大學的數據科學教授，也是數據科學、工程和分析研究所（IDE+A）的成員。他學習了物理學、統計學和數據科學，他的研究興趣包括黑盒優化問題的景觀分析和工業機器學習應用。他是探索性景觀分析方法的開發者之一，該方法用於表徵連續函數景觀。