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A senior high textbook focusing on the North American forest regions; classification, anatomy, and diseases of trees; forest management and products; and urban forestry.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Introduction to Forestry Science, Second Edition, is an introductory forestry book that covers the principles and practices of forest management that are commonly practiced in the United States. Processing of wood and forest products is an important unit that points out the economic impact of forests on the U.S. economy. A complete chapter focuses on government historical events and policies that have influenced forest management practices since North America was colonized. Attention is given to regional differences in forests and forest management. Laws and regulations that govern the use of forests are also addressed. This text follows an applied science approach that integrates science principles with forestry practices. Science topics are woven throughout, and reinforced through the student s need to know feature. Topics covered include cell structure and function, cell growth and reproduction, elemental cycles, principles of ecology, the food pyramid, plant anatomy and physiology, entomology, water quality, air quality and preservation of endangered species. In addition, career options related to forests and forest management are explored. Other features include terms for understanding, student-learning objectives, chapter summaries, review questions, learning activities, and profiles on events and issues related to forests and science. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
The Laboratory Manual is a valuable tool designed to enhance your understanding of the concepts discussed in the core textbook. Each chapter begins wth a list of objectives, followed by activities designed to achieve the objectives, and concludes with a list of references for further study.
This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.