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| decision analysis tree example: Decision Trees for Decision Making John F. Magee, 1964 |
| decision analysis tree example: Interpretable Machine Learning Christoph Molnar, 2020 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. |
| decision analysis tree example: Data Mining with Decision Trees Lior Rokach, Oded Z. Maimon, 2008 This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection |
| decision analysis tree example: Data Mining and Knowledge Discovery Handbook Oded Maimon, Lior Rokach, 2006-05-28 Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. |
| decision analysis tree example: Ethnographic Decision Tree Modeling Christina H. Gladwin, 1989-09 Why do people in a certain group behave the way they do? And, more importantly, what specific criteria was used by the group in question? This book presents a method for answering these questions. |
| decision analysis tree example: Decision Trees for Business Intelligence and Data Mining Barry De Ville, 2006 This example-driven guide illustrates the application and operation of decision trees in data mining, business intelligence, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements other business intelligence applications. |
| decision analysis tree example: Confronting Climate Uncertainty in Water Resources Planning and Project Design Patrick A. Ray, Casey M. Brown, 2015-08-20 Confronting Climate Uncertainty in Water Resources Planning and Project Design describes an approach to facing two fundamental and unavoidable issues brought about by climate change uncertainty in water resources planning and project design. The first is a risk assessment problem. The second relates to risk management. This book provides background on the risks relevant in water systems planning, the different approaches to scenario definition in water system planning, and an introduction to the decision-scaling methodology upon which the decision tree is based. The decision tree is described as a scientifically defensible, repeatable, direct and clear method for demonstrating the robustness of a project to climate change. While applicable to all water resources projects, it allocates effort to projects in a way that is consistent with their potential sensitivity to climate risk. The process was designed to be hierarchical, with different stages or phases of analysis triggered based on the findings of the previous phase. An application example is provided followed by a descriptions of some of the tools available for decision making under uncertainty and methods available for climate risk management. The tool was designed for the World Bank but can be applicable in other scenarios where similar challenges arise. |
| decision analysis tree example: Decision Trees for Analytics Using SAS Enterprise Miner Barry De Ville, Padraic Neville, 2019-07-03 Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. This book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high-performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice. Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book. |
| decision analysis tree example: Managerial Decision Analysis Danny Samson, 1992-05-19 This text focuses on how decision analysis can be used to support the managerial decision process. It supports professors and students in the classroom with extensive case studies and problem sets, and with Arborist software and documentation. |
| decision analysis tree example: Using Information to Develop a Culture of Customer Centricity David Loshin, Abie Reifer, 2013-11-22 Using Information to Develop a Culture of Customer Centricity sets the stage for understanding the holistic marriage of information, socialization, and process change necessary for transitioning an organization to customer centricity. The book begins with an overview list of 8-10 precepts associated with a business-focused view of the knowledge necessary for developing customer-oriented business processes that lead to excellent customer experiences resulting in increased revenues. Each chapter delves into each precept in more detail. |
| decision analysis tree example: Decision Analysis for Managers, Second Edition David Charlesworth, 2017-04-11 Everybody has to make decisions-they are unavoidable. However, we receive little or no education or training on how to make decisions. Business decisions are difficult: which people to hire, which product lines or facilities to expand, which proposal to accept, how much R&D to invest in, which environmental projects are high priority, etc. Personal decisions (college, getting married, changing jobs, buying a house, retiring, dealing with a health problem) can be even more difficult. This book gives you the tools you need toÉClarify and reach alignment on goals and objectives; Understand trade-offs associated with reaching those objectives; Develop and examine alternatives; Systematically analyze the effects of risk and uncertainty, and; Maximize the chances of achieving your goals. Success (getting what you want) depends on luck and good decision-making. You can't control your luck, but you can maximize your odds by making the best possible decisions, and this book gets you there. The author organizes and presents otherwise formal decision-making tools in an intuitively understandable fashion. The presentation is informal, but the concepts and tools are research-based and formally accepted. Whether you are a business owner, a manager or team leader, or a senior professional, these tools will help both your personal and your business life. |
| decision analysis tree example: Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, 2015-04-02 In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder |
| decision analysis tree example: Discovering Knowledge in Data Daniel T. Larose, 2005-01-28 Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a white box methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online. |
| decision analysis tree example: Continuous Discovery Habits Teresa Torres, 2021-05-19 If you haven't had the good fortune to be coached by a strong leader or product coach, this book can help fill that gap and set you on the path to success. - Marty Cagan How do you know that you are making a product or service that your customers want? How do you ensure that you are improving it over time? How do you guarantee that your team is creating value for your customers in a way that creates value for your business? In this book, you'll learn a structured and sustainable approach to continuous discovery that will help you answer each of these questions, giving you the confidence to act while also preparing you to be wrong. You'll learn to balance action with doubt so that you can get started without being blindsided by what you don't get right. If you want to discover products that customers love-that also deliver business results-this book is for you. |
| decision analysis tree example: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| decision analysis tree example: Decision Analysis for Management Judgment Paul Goodwin, George Wright, 2014-05-12 Decision Analysis for Management Judgment is unique in its breadth of coverage of decision analysis methods. It covers both the psychological problems that are associated with unaided managerial decision making and the decision analysis methods designed to overcome them. It is presented and explained in a clear, straightforward manner without using mathematical notation. This latest edition has been fully revised and updated and includes a number of changes to reflect the latest developments in the field. |
| decision analysis tree example: Statistics and Probability Theory Michael Havbro Faber, 2012-03-26 This book provides the reader with the basic skills and tools of statistics and probability in the context of engineering modeling and analysis. The emphasis is on the application and the reasoning behind the application of these skills and tools for the purpose of enhancing decision making in engineering. The purpose of the book is to ensure that the reader will acquire the required theoretical basis and technical skills such as to feel comfortable with the theory of basic statistics and probability. Moreover, in this book, as opposed to many standard books on the same subject, the perspective is to focus on the use of the theory for the purpose of engineering model building and decision making. This work is suitable for readers with little or no prior knowledge on the subject of statistics and probability. |
| decision analysis tree example: Real World Project Management Richard Perrin, 2008-03-31 If you're a project manager, you need this guide to fill in the gaps in the PM canon. The Project Management Institute's Body of Knowledge, fails to fully explain certain PM tools and how they work, among other failures. Real-World Project Management fills in those major gaps with irreverence, wit, and wisdom. For any kind of project you’re managing, this book presents the high-quality tools and tactics you need to succeed. |
| decision analysis tree example: Classification and Regression Trees Leo Breiman, 2017-10-19 The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties. |
| decision analysis tree example: Machine Learning with Swift Oleksandr Sosnovshchenko, Oleksandr Baiev, 2018-02-28 Leverage the power of machine learning and Swift programming to build intelligent iOS applications with ease Key Features Implement effective machine learning solutions for your iOS applications Use Swift and Core ML to build and deploy popular machine learning models Develop neural networks for natural language processing and computer vision Book Description Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves. What you will learn Learn rapid model prototyping with Python and Swift Deploy pre-trained models to iOS using Core ML Find hidden patterns in the data using unsupervised learning Get a deeper understanding of the clustering techniques Learn modern compact architectures of neural networks for iOS devices Train neural networks for image processing and natural language processing Who this book is for iOS developers who wish to create smarter iOS applications using the power of machine learning will find this book to be useful. This book will also benefit data science professionals who are interested in performing machine learning on mobile devices. Familiarity with Swift programming is all you need to get started with this book. |
| decision analysis tree example: Decision Analysis for Managers David Charlesworth, 2013-03-13 Everybody has to make decisions—they are unavoidable. Yet we receive little or no education or training on how to make decisions. Business decisions can be dif_ cult: which people to hire, which product lines or facilities to expand and which to sell or shut down, which bid or proposal to accept, which process to implement, how much R&D to invest in, which environmental projects should receive the highest priority, etc. This book gives you all the tools you need to... • clarify and reach alignment on goals and objectives and understand trade-offs in reaching those goals, • develop and examine alternatives, • systematically analyze the effects of risk and uncertainty, and • maximize the chances of achieving your goals and objectives. Success (getting what you want) depends on luck and good decision making. You can’t control your luck, but you can maximize your odds by making the best possible decisions, and this book gets you there. Broadly speaking, this book organizes and presents otherwise formal decision-making tools in an intuitively understandable fashion. The presentation is informal, but the concepts and tools are research-based and formally accepted. |
| decision analysis tree example: Fundamentals of Predictive Analytics with JMP, Second Edition Ron Klimberg, B. D. McCullough, 2017-12-19 Going beyond the theoretical foundation, this step-by-step book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. -- |
| decision analysis tree example: Bayesian Decision Analysis Jim Q. Smith, 2010-09-23 Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics. |
| decision analysis tree example: The Information System Consultant's Handbook William S. Davis, David C. Yen, 2019-04-30 The Information System Consultant's Handbook familiarizes systems analysts, systems designers, and information systems consultants with underlying principles, specific documentation, and methodologies. Corresponding to the primary stages in the systems development life cycle, the book divides into eight sections: Principles Information Gathering and Problem Definition Project Planning and Project Management Systems Analysis Identifying Alternatives Component Design Testing and Implementation Operation and Maintenance Eighty-two chapters comprise the book, and each chapter covers a single tool, technique, set of principles, or methodology. The clear, concise narrative, supplemented with numerous illustrations and diagrams, makes the material accessible for readers - effectively outlining new and unfamiliar analysis and design topics. |
| decision analysis tree example: End-to-End Data Science with SAS James Gearheart, 2020-06-26 Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step. Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user’s guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model’s effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models. |
| decision analysis tree example: Automatic Design of Decision-Tree Induction Algorithms Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas, 2015-02-04 Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. Automatic Design of Decision-Tree Induction Algorithms would be highly useful for machine learning and evolutionary computation students and researchers alike. |
| decision analysis tree example: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| decision analysis tree example: Examples & Explanations for Dispute Resolution Michael L. Moffitt, Andrea Kupfer Schneider, 2019-10-16 Highly respected ADR authors Michael Moffitt and Andrea Schneider bring their considerable experience and expertise to the proven-effective Examples & Explanations series pedagogy. Dispute Resolution, Fourth Edition combines introductions to theory with practical exercises in decision analysis, problem solving, and various forms of conflict resolution. New to the 4th Edition: Updated and streamlined coverage of arbitration, in light of recent Supreme Court cases, including New Prime, DirectTV, and Schein v. Archer Updated treatment of mediation confidentiality, ethics, and the enforcement of mediation agreements Includes materials on fraud and other negotiation misconduct Includes recent U.S. Supreme Court opinions, state and federal legislative changes, and common contractual modifications Cites and references to principal cases used in most leading casebooks Updated examples throughout Professors and students will benefit from: Practical and broad coverage of the three principal areas of dispute resolution practice Up-to-date coverage of recent court developments Entertaining examples designed to make significant concepts engaging and memorable A modular approach that permits the materials to be engaged with in any sequence, and be adapted easily to any textbook or casebook |
| decision analysis tree example: Presto Sketching Ben Crothers, 2017-10-19 Do you feel like your thoughts, ideas, and plans are being suffocated by a constant onslaught of information? Do you want to get those great ideas out of your head, onto the whiteboard and into everyone else’s heads, but find it hard to start? No matter what level of sketching you think you have, Presto Sketching will help you lift your game in visual thinking and visual communication. In this practical workbook, Ben Crothers provides loads of tips, templates, and exercises that help you develop your visual vocabulary and sketching skills to clearly express and communicate your ideas. Learn techniques like product sketching, storyboarding, journey mapping, and conceptual illustration. Dive into how to use a visual metaphor (with a library of 101 visual metaphors), as well as tips for capturing and sharing your sketches digitally, and developing your own style. Designers, product managers, trainers, and entrepreneurs will learn better ways to explore problems, explain concepts, and come up with well-defined ideas - and have fun doing it. |
| decision analysis tree example: Data-Guided Healthcare Decision Making Ramalingam Shanmugam, 2023-05-31 This book effectively exposes and illustrates the ideas and tools for optimal healthcare decisions taken from evidence. |
| decision analysis tree example: Decision Analysis Fouad Sabry, 2023-06-27 What Is Decision Analysis The term decision analysis (DA) refers to the academic field that encompasses the theory, technique, and professional practice that are required to tackle significant decisions in an organized fashion. It is possible to prescribe a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision. Additionally, decision analysis includes many procedures, methods, and tools for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, as well as for other corporate and non-corporate stakeholders. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Decision Analysis Chapter 2: Decision Theory Chapter 3: Multiple-criteria Decision Analysis Chapter 4: Expected Value of Sample Information Chapter 5: Decision-making Software Chapter 6: Robust Decision-making Chapter 7: Expected Value of Including Uncertainty Chapter 8: Decision Quality Chapter 9: Value Tree Analysis Chapter 10: Bayesian Inference in Marketing (II) Answering the public top questions about decision analysis. (III) Real world examples for the usage of decision analysis in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of decision analysis' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of decision analysis. |
| decision analysis tree example: Finance for Engineers Frank Crundwell, 2008-03-11 With flair and an originality of approach, Crundwell brings his considerable experience to bear on this crucial topic. Uniquely, this book discusses the technical and financial aspects of decision-making in engineering and demonstrates these through case studies. It’s a hugely important matter as, of course, engineering solutions and financial decisions are intimately tied together. The best engineers combine the technical and financial cases in determining new solutions to opportunities, challenges and problems. To get your project approved, no matter the size of it, the financial case must be clear and compelling. This book provides a framework for engineers and scientists to undertake financial evaluations and assessments of engineering or production projects. |
| decision analysis tree example: Syntactic Structures Noam Chomsky, 2020-05-18 No detailed description available for Syntactic Structures. |
| decision analysis tree example: Hands-On Machine Learning with R Brad Boehmke, Brandon M. Greenwell, 2019-11-07 Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data. |
| decision analysis tree example: The PROACT® Root Cause Analysis Kenneth C. Latino, Mark A. Latino, Robert J. Latino, 2020-09-10 Root Cause Analysis, or RCA, What is it? Everyone uses the term, but everyone does it differently. How can we have any uniformity in our approach, much less accurately compare our results, if we’re applying different definitions? At a high level, we will explain the difference between RCA and Shallow Cause Analysis, because that is the difference between allowing a failure to recur or dramatically reducing the risk of recurrence. In this book, we will get down to basics about RCA, the fundamentals of blocking and tackling, and explain the common steps of any investigative occupation. Common investigation steps include: Preserving evidence (data)/not allowing hearsay to fly as fact Organizing an appropriate team/minimizing potential bias Analyzing the events/reconstructing the incident based on actual evidence Communicating findings and recommendations/ensuring effective recommendations are actually developed and implemented Tracking bottom-line results/ensuring that identified, meaningful metrics were attained We explore, Why don’t things always go as planned? When our actual plans deviate from our intended plans, we usually experience some type of undesirable or unintended outcome. We analyze the anatomy of a failure (undesirable outcome) and provide a step-by-step guide to conducting a comprehensive RCA based on our 3+ decades of applying RCA as we have successfully practiced it in the field. This book is written as a how-to guide to effectively apply the PROACT® RCA methodology to any undesirable outcome, is directed at practitioners who have to do the real work, focuses on the core elements of any investigation, and provides a field-proven case as a model for effective application. This book is for anyone charged with having a thorough understanding of why something went wrong, such as those in EH&S, maintenance, reliability, quality, engineering, and operations to name just a few. |
| decision analysis tree example: Applied Informatics and Cybernetics in Intelligent Systems Radek Silhavy, 2020-08-07 This book gathers the refereed proceedings of the Applied Informatics and Cybernetics in Intelligent Systems Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Modern cybernetics and computer engineering in connection with intelligent systems are an essential aspect of ongoing research. This book addresses these topics, together with automation and control theory, cybernetic applications, and the latest research trends. |
| decision analysis tree example: Value of Information in the Earth Sciences Jo Eidsvik, Tapan Mukerji, Debarun Bhattacharjya, 2015-11-19 Gathering the right kind and the right amount of information is crucial for any decision-making process. This book presents a unified framework for assessing the value of potential data gathering schemes by integrating spatial modelling and decision analysis, with a focus on the Earth sciences. The authors discuss the value of imperfect versus perfect information, and the value of total versus partial information, where only subsets of the data are acquired. Concepts are illustrated using a suite of quantitative tools from decision analysis, such as decision trees and influence diagrams, as well as models for continuous and discrete dependent spatial variables, including Bayesian networks, Markov random fields, Gaussian processes, and multiple-point geostatistics. Unique in scope, this book is of interest to students, researchers and industry professionals in the Earth and environmental sciences, who use applied statistics and decision analysis techniques, and particularly to those working in petroleum, mining, and environmental geoscience. |
| decision analysis tree example: Theory and Practice in Policy Analysis M. Granger Morgan, 2017-10-12 Many books instruct readers on how to use the tools of policy analysis. This book is different. Its primary focus is on helping readers to look critically at the strengths, limitations, and the underlying assumptions analysts make when they use standard tools or problem framings. Using examples, many of which involve issues in science and technology, the book exposes readers to some of the critical issues of taste, professional responsibility, ethics, and values that are associated with policy analysis and research. Topics covered include policy problems formulated in terms of utility maximization such as benefit-cost, decision, and multi-attribute analysis, issues in the valuation of intangibles, uncertainty in policy analysis, selected topics in risk analysis and communication, limitations and alternatives to the paradigm of utility maximization, issues in behavioral decision theory, issues related to organizations and multiple agents, and selected topics in policy advice and policy analysis for government. |
| decision analysis tree example: The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2013-11-11 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| decision analysis tree example: C4.5 J. Ross Quinlan, 1993 This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes. |
DECISION Definition & Meaning - Merriam-Webster
The meaning of DECISION is the act or process of deciding. How to use decision in a sentence.
DECISION | English meaning - Cambridge Dictionary
DECISION definition: 1. a choice that you make about something after thinking about several possibilities: 2. the…. Learn more.
DECISION Definition & Meaning | Dictionary.com
Decision definition: the act or process of deciding; deciding; determination, as of a question or doubt, by making a judgment.. See examples of DECISION used in a sentence.
decision noun - Definition, pictures, pronunciation and usage …
Definition of decision noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Decision - definition of decision by The Free Dictionary
1. the act or process of deciding. 2. the act of making up one's mind: a difficult decision. 3. something that is decided; resolution. 4. a judgment, as one pronounced by a court. 5. the …
What does Decision mean? - Definitions.net
What does Decision mean? This dictionary definitions page includes all the possible meanings, example usage and translations of the word Decision. A choice or judgement. Firmness of …
decision - Wiktionary, the free dictionary
Jun 7, 2025 · (choice or judgment): Most often, to decide something is to make a decision; however, other possibilities exist as well. Many verbs used with destination or conclusion, such …
SUPREME COURT OF THE UNITED STATES
3 days ago · judgment” rule articulated by the Eighth Circuit in its 1982 decision in Monahan, in which the Eighth Circuit reasoned that to prove dis-crimination under the Rehabilitation Act in …
Decision-making - Wikipedia
In psychology, decision-making (also spelled decision making and decisionmaking) is regarded as the cognitive process resulting in the selection of a belief or a course of action among several …
Decision - Definition, Meaning & Synonyms - Vocabulary.com
To make a decision is to make up your mind about something. To act with decision is to proceed with determination, which might be a natural character trait.
DECISION Definition & Meaning - Merriam-Webster
The meaning of DECISION is the act or process of deciding. How to use decision in a sentence.
DECISION | English meaning - Cambridge Dictionary
DECISION definition: 1. a choice that you make about something after thinking about several possibilities: …
DECISION Definition & Meaning | Dictionary.com
Decision definition: the act or process of deciding; deciding; determination, as of a question or doubt, by making a …
decision noun - Definition, pictures, pronunciation and u…
Definition of decision noun in Oxford Advanced American Dictionary. Meaning, pronunciation, picture, …
Decision - definition of decision by The Free Dictionary
1. the act or process of deciding. 2. the act of making up one's mind: a difficult decision. 3. something that is …
