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| examples of decision tree analysis: Decision Trees for Decision Making John F. Magee, 1964 |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: Risk and Rigor Marjory Aaron, 2019-04 Clear step-by-step explanations of decision tree analysis along with guidance on assessing probabilities, tangible and intangible costs, and damage ranges. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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 |
| examples of decision tree analysis: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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 |
| examples of decision tree analysis: Data Mining with Decision Trees Lior Rokach, 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 . Sample Chapter(s). Chapter 1: Introduction to Decision Trees (245 KB). Chapter 6: Advanced Decision Trees (409 KB). Chapter 10: Fuzzy Decision Trees (220 KB). Contents: Introduction to Decision Trees; Growing Decision Trees; Evaluation of Classification Trees; Splitting Criteria; Pruning Trees; Advanced Decision Trees; Decision Forests; Incremental Learning of Decision Trees; Feature Selection; Fuzzy Decision Trees; Hybridization of Decision Trees with Other Techniques; Sequence Classification Using Decision Trees. Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management. |
| examples of decision tree analysis: 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 |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: Machine Learning Pocket Reference Matt Harrison, 2019-08-27 With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines |
| examples of decision tree analysis: Master Machine Learning Algorithms Jason Brownlee, 2016-03-04 You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step. |
| examples of decision tree analysis: Machine Learning with R Brett Lantz, 2013-10-25 Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required. |
| examples of decision tree analysis: Tree Models of Similarity and Association James E. Corter, 1996-04-02 This book describes how matrices of similarities or associations among entities can be modelled using trees in order to explain some of the issues that arise in performing similarity relations analyses and interpreting the results correctly. |
| examples of decision tree analysis: Machine Intelligence and Soft Computing Debnath Bhattacharyya, N. Thirupathi Rao, 2021-01-21 This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2020), held jointly by Vignan’s Institute of Information Technology, Visakhapatnam, India and VFSTR Deemed to be University, Guntur, AP, India during 03-04 September 2020. Topics covered in the book include the artificial neural networks and fuzzy logic, cloud computing, evolutionary algorithms and computation, machine learning, metaheuristics and swarm intelligence, neuro-fuzzy system, soft computing and decision support systems, soft computing applications in actuarial science, soft computing for database deadlock resolution, soft computing methods in engineering, and support vector machine. |
| examples of decision tree analysis: Syntactic Structures Noam Chomsky, 2020-05-18 No detailed description available for Syntactic Structures. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: 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. |
| examples of decision tree analysis: Advances in Patient Safety Kerm Henriksen, 2005 v. 1. Research findings -- v. 2. Concepts and methodology -- v. 3. Implementation issues -- v. 4. Programs, tools and products. |
| examples of decision tree analysis: Machine Learning Essentials Alboukadel Kassambara, 2018-03-10 Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples. |
| examples of decision tree analysis: Building Better Models with JMP Pro Jim Grayson, Sam Gardner, Mia Stephens, 2015-08-01 Building Better Models with JMP® Pro provides an example-based introduction to business analytics, with a proven process that guides you in the application of modeling tools and concepts. It gives you the what, why, and how of using JMP® Pro for building and applying analytic models. This book is designed for business analysts, managers, and practitioners who may not have a solid statistical background, but need to be able to readily apply analytic methods to solve business problems. In addition, this book will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP Pro is an applied book on business analytics and modeling that introduces a simple methodology for managing and executing analytics projects. No prior experience with JMP is needed. Make more informed decisions from your data using this newest JMP book. |
| examples of decision tree analysis: Project Valuation Using Real Options Prasad Kodukula, Chandra Papudesu, 2006-07-15 Business leaders are frequently faced with investment decisions on new and ongoing projects. The challenge lies in deciding what projects to choose, expand, contract, defer, or abandon, and which method of valuation to use is the key tool in the process. This title presents a step-by-step, practical approach to real options valuation to make it easily understandable by practitioners as well as senior management. This systematic approach to project valuation helps you minimize upfront investment risks, exercise flexibility in decision making, and maximize the returns. Whereas the traditional decision tools such as discounted cash flow/net present value (DCF/NPV) analysis assume a “fixed” path ahead, real options analysis offers more flexible strategies. Considered one of the greatest innovations of modern finance, the real options approach is based on Nobel-prize winning work by three MIT economists, Fischer Black, Robert Merton, and Myron Scholes. |
| examples of decision tree analysis: Data Mining and Machine Learning Applications Rohit Raja, Kapil Kumar Nagwanshi, Sandeep Kumar, K. Ramya Laxmi, 2022-03-02 DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly. |
| examples of decision tree analysis: Contemporary Security Management David Patterson, John Fay, 2017-10-27 Contemporary Security Management, Fourth Edition, identifies and condenses into clear language the principal functions and responsibilities for security professionals in supervisory and managerial positions. Managers will learn to understand the mission of the corporate security department and how the mission intersects with the missions of other departments. The book assists managers with the critical interactions they will have with decision makers at all levels of an organization, keeping them aware of the many corporate rules, business laws, and protocols of the industry in which the corporation operates. Coverage includes the latest trends in ethics, interviewing, liability, and security-related standards. The book provides concise information on understanding budgeting, acquisition of capital equipment, employee performance rating, delegated authority, project management, counseling, and hiring. Productivity, protection of corporate assets, and monitoring of contract services and guard force operations are also detailed, as well as how to build quality relationships with leaders of external organizations, such as police, fire and emergency response agencies, and the Department of Homeland Security. - Focuses on the evolving characteristics of major security threats confronting any organization - Assists aspirants for senior security positions in matching their personal expertise and interests with particular areas of security management - Includes updated information on the latest trends in ethics, interviewing, liability, and security-related standards |
| examples of decision tree analysis: Decision Trees and Random Forests Mark Koning, Chris Smith, 2017-10-04 If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services. They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you. |
| examples of decision tree analysis: Decision Forests Antonio Criminisi, Jamie Shotton, Ender Konukoglu, 2012-03 Presents a unified, efficient model of random decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images, automatic diagnosis from radiological scans and document analysis. |
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 …
Events - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 …
Events - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Decision Trees Another Example Problem - University of …
What if some examples missing values of A?
D8.1 Problem Tree Analysis – Procedure and Example - Eawag
Problem tree analysis helps stakeholders to establish a realistic overview and awareness of the problem by ing the fundamental causes and their most identify important effects.
Decision Trees - University of Pennsylvania
Decision Tree • A possible decision tree for the data: • Each internal node: test one aribute X i • Each branch from a node: selects one value for X i • Each leaf node: predict Y (or ) Based on …
Decision Trees Example Problem - CMU School of Computer …
Consider the following data, where the Y label is whether or not the child goes out to play. Play? Step 2: Choose which feature to split with! Step 4: Choose feature for each node to split on! …
Decision trees: examples for self-study—solutions
You should be looking at a tree like this: This is a classic problem with many, many writeups, drawn mostly from Mitchell, so you should be able to find more information on any part of it. …
Decision Tree Exercises - JMU
Decision Tree Exercises 1. Gini Impurity The goal in building a decision tree is to create the smallest possible tree in which each leaf node contains training data from only one class. In …
Lecture 19: Decision trees - Stanford University
How is a decision tree built? 1. Select a region Rk, a predictor Xj, and a splitting point s, such that splitting Rk with the criterion Xj < s produces the largest decrease in RSS: 2. Redefine the …
Marcelo Coca Perraillon - College of Liberal Arts and Sciences
A popular book on decision modeling (Briggs et al, 2006) has tons of examples of models using Excel (relevant chapters in Canvas) For learning, Excel is the best tool.
UNIT 3: BUSINESS ANALYSIS AND STRATEGY
Allow managers to analyse fully the possible consequences and risks of a decision. Provide a framework to quantify the values of outcomes and the probabilities of achieving them. Decision …
Decision Tree Classification - jarrar.info
Regression algorithms can draw a boundary line between the data. Decision Trees introduces a threshold for each axis individually. But if keep introducing axis-aligned splits (the tree …
DECISION TREES: How to Construct Them and How to Use …
Decision Trees An RVL Tutorial by Avi Kak This tutorial will demonstrate how the notion of entropy can be used to construct a decision tree in which the feature tests for making a …
Project Analysis using Decision Trees and Options
Project Analysis using Decision Trees and Options Decisions on projects always involve uncertainty. How do you deal with it? • Probabilistic Analysis: Assess risk by estimating project …
Use Decision Trees to Make Important Project Decisions1 By …
Decision tree methods can help make decisions when the results are not known with certainty by comparing their expected value or expected utility to the organization. The term “expected …
DECISION TREES AND INFLUENCE DIAGRAMS - University of …
In this section, we describe a decision tree representation and solution of the Medical Diagnosis problem. Also, we describe the strengths and weaknesses of the decision tree representation …
Interpreting a Decision Tree Analysis of a Lawsuit - Litigation …
More and more attorneys are evaluating lawsuits by performing decision tree analyses (also known as risk analyses). These analyses can be used in a variety of ways: To plan a cost …
Decision Tree Analysis for the Risk Averse Organization
This paper summarizes the traditional decision tree analysis based on expected monetary value (EMV) and contrasts that approach to the risk averse organization’s use of expected utility (E(U)).
ROOT CAUSE ANALYSIS AND DECISION TREES TOOLS FOR …
TOOLS FOR CREATING DECISION TREES • Software Options: • Microsoft Excel: Simple decision tree diagrams. • Lucidchart: User-friendly decision tree creation. • Xmind: Mind …
Use Decision Trees to Make Important Project Decisions
We can illustrate decision tree analysis by considering a common decision faced on a project. We are the prime contractor and there is a penalty in our contract with the main client for every …
Decision Tree Analysis for Law Practice
DECISION TREE ANALYSIS 407 I. INTRODUCTION “I shall present one recently developed concept called the “decision tree,” which has tremendous potential as a decision-making tool. …
Use Decision Trees to Make Important Project Decisions
Simple Decision – One Decision Node and Two Chance Nodes . We can illustrate decision tree analysis by considering a common decision faced on a project. We are the prime contractor …
A Fault Tree Analysis (FTA) Based Approach for Construction …
the Fault Tree Analysis (FTA) method is presented in section 3 and Construction Safety Fault Tree alizedin is visu section 4. After that, the way qualitative and quantitative analysis can be …
Decision Trees - World Scientific Publishing Co Pte Ltd
Decision trees can be interpreted as a formalization of common sense for more complex decision-making situations under uncertainty. We explain the basic idea of the approach with a few …
Creating Decision Trees to Assess Cost-Effectiveness in …
effectiveness. Decision analysis models have been used to compare screening strategies, diagnostic techniques and treatment plans. As cost-effectiveness gains increasing importance, …
Tree Based Methods: Regression Trees - Duke University
BasicsofDecision(Predictions)Trees I Thegeneralideaisthatwewillsegmentthepredictorspace intoanumberofsimpleregions. I Inordertomakeapredictionforagivenobservation,we ...
Introduction to decision modelling - healtheconomics.org
Decision Tree Example •Illustrative example: Heparin for the prevention of deep vein thrombosis (DVT) in hip replacement patients •Patients are at risk of DVT(and pulmonary embolism) post …
Decision Tree Practice Problems - IIT Kharagpur
can be used in different branches on a given level of the tree. For the second tree, follow the Decision-Tree-Learning algorithm. As your Choose-Attribute function, choose the attribute with …
15.097 Lecture 8: Decision trees - MIT OpenCourseWare
How to build a decision tree: Start at the top of the tree. Grow it by \splitting" attributes one by one. To determine which attribute to split, look at \node impurity." Assign leaf nodes the …
UNIT – III : DECISION THEORY - Guru Nanak College
UNIT –V : DECISION THEORY Syllabus: Decision theory: Risk and uncertainty in decision-making –minimax, maximin and regret criterion –Hurwicz and Laplace criteria in decision …
Chapter 3 System Analysis Event Tree Analysis - CERN
An event tree analysis (ETA) is an inductive procedure that shows all possible outcomes resulting from an accidental ... Some examples of additional events/factors were given on a previous …
Decision Trees Example Problem - CMU School of Computer …
Decision Trees Example Problem Consider the following data, where the Y label is whether or not the child goes out to play. 5 7 Day Weather Temperature Humidity Wind Play? ... Final Tree! …
Chapter 14 Decision Tree Analysis and Utility Theory - Springer
Decision tree analysis is examined in the first part of the chapter, after which utility theorywill be discussed. 14.2 Decision Tree Analysis 14.2.1 Decision, Event and Terminal Nodes A decision …
Economic Risk and Decision Analysis for Oil and Gas …
2 Introduction Decision Tree Analysis is one of the tools available to aid in the decision making process. Decision Tree is diagrammatic representation of decision situation that …
The Decision Tree Application in Agricultural Development
Using the decision tree method for data classification, it normally takes two steps. First, an initial decision tree should be generated from the training sets. Secondly, the above decision tree …
Fault Tree Analysis (FTA) and Event Tree Analysis (ETA)
tree. The author seems to think that smoking is a major cause of ignition. There are probably many more examples of ignition causes that have not been considered. If you are interested in …
Introduction to Decision Analysis - UW Faculty Web Server
Examples: • Drilling for Oil • Developing a New Product • Newsvendor Problem • Producing a Movie . Session #7 Page 2 ... Introduction to Decision Analysis Using a Decision Tree to …
Hazard Analysis and Critical Control Points Guide - PQRI
Training Guide: Hazard Analysis and Critical Control Points (HACCP) Page 6 of 8 The first question in the Codex Decision tree addresses whether or not there is a measure in place to …
The Basics of Healthcare Failure Mode and Effect Analysis
HFMEA Step 4 - Hazard Analysis HFMEA Step 5 - Identify Actions and Outcomes Failure Mode: First Evaluate failure mode before determining potential causes Potential Causes Scoring …
Fuzzy Decision Trees: Issues and Methods - cs.umsl.edu
Decision trees are made of two major components: a procedure to build the symbolic tree, and an inference procedure for decision making. This paper describes the tree-building procedure for …
Decision and Regression Trees - Guide to Intelligent Data …
What is a Decision Tree 9 −Decision trees aim to find a hierarchical structure to explain how different areas in the input space correspond to different outcomes. −Useful for data with a lot …
Kapil Mittal, Dinesh Khanduja, Puran Chandra Tewari
Used the decision tree analysis for the system reliability decision in a microelectronics company 7 (Dyer & Lorber 1982) Evaluated the potential subcontractors for project planning purposes
Decision Trees and Quality Control Decisions - CORE
modified decision tree analysis to scenario and game trees suggested by other researchers as modifications to the traditional analysis. Finally, section seven provides a summary and …
DECISION TREES: How to Construct Them and How to Use …
Decision Trees An RVL Tutorial by Avi Kak This tutorial will demonstrate how the notion of entropy can be used to construct a decision tree in which the feature tests for making a …
sa jan13 f5 decision trees - ACCA Global
Once the decision tree has been drawn, the decision must then be evaluated. Evaluating the decision When a decision tree is evaluated, the evaluation starts on the right-hand side of the …
Decision Tree Classification - jarrar.info
Split (node, {examples} ): 1. A ← the best attribute for splitting the {examples} 2. Decision attribute for this node ← A 3. For each value of A, create new child node 4. Split training {examples} to …
Introduction to Decision Analysis - UCREANOP
19.3 Decision-Tree Analysis CHAPTER OUTCOMES After studying the material in Chapter 19, you should be able to: 1. Describe the decision-making environments of certainty and …
DECISION ANALYSIS IN PUBLIC HEALTH - rees-france.com
decision analysis Decision analysis is an approach used to construct and structure decisions Quantitative support for decision-makers in wide range of disciplines Systematic quantitative …
ANNEX 1: HACCP: THE METHOD AND EXAMPLES ANNEX 1.1.
refer to the HACCP plan on the following pages and the hazard analysis tables in Annex 2 and 3. Annex 1.2.7. Determining the critical points for controlling the hazards: the C.C.P.s (Principle …
DECISION TREES AND INFLUENCE DIAGRAMS - University of …
decision tree representation depicts all scenarios explicitly, it is computationally infeasible to represent a decision problem with, say, 50 variables. 3.4. Strengths and Weaknesses of the …
Uprooting the decision tree - Advocate Magazine
Decision tree analysis, also known as risk analysis, is a tree-like flow chart with nodes and branches that lists out each possi-ble outcome of a decision, allows you to assign probabilities …
Project Analysis using Decision Trees and Options
• Changes in the assumptions can change the decision Sensitivity Analysis examines the sensitivity of a decision rule (NPV, IRR, etc.) to changes in the assumptions underlying a …
Decision Trees - University of Pennsylvania
Basic Algorithm for Top-Down InducIon of Decision Trees [ID3, C4.5 by Quinlan] node = root of decision tree Main loop: 1. A ß the “best” decision aribute for the next node. 2. Assign A as …
ROOT CAUSE ANALYSIS AND DECISION TREES TOOLS FOR …
BEST PRACTICES FOR USING DECISION TREES • Keep It Simple: Avoid overcomplicating the tree. • Use Clear Criteria: Ensure all decision points are well-defined. • Update Regularly: …
Machine Learning: Decision Trees - University of …
A Decision Tree • A decision tree has 2 kinds of nodes 1. Each leaf node has a class label, determined by majority vote of training examples reaching that leaf. 2. Each internal node is a …
Problem analysis approaches - World Health Organization
Problem-analysis approaches 1. The Problem Tree The problem tree is a visual method of analysing a problem. The tree maps the links between the main issue and its resulting …
Shopper Insights Consumer Decision Trees - Dechert-Hampe
DHC has Developed a Unique and Proven Consumer Decision Tree Methodology • Research study design for consumer decision tree – In-store (or on -line) intercepts with qualified …
An Introduction to Fault Tree Analysis - The Xerte Project
In recent years Figure 1. Typical Fault Tree Featuresan alternative to kinetic tree theory for efficient and accurate fault tree quantification has been developed known as the Binary …
408 Using Decision-tree Analysis to Intelligently Manage …
Decision tree analysis: a better alternative • ! The client’s world • ! Benefits of analytical decision analysis • ! Limitations Analysis to Effectively Manage Litigation What outside counsel offer ! …
Module 10 Sequential Decision Making - Purdue University
Sequential Decision Making is an activity of gathering information about alternatives to compare and choose the best alternative. It consists of sequential decisions to:
Decision tree dataset example
Decision tree statistics examples. Decision tree for data analysis. What is decision tree diagram. Decision tree examples. Decision tree large data set. Title: Decision tree dataset example …
Fundamentals of Decision Theory - University of Washington
–A bad decision may occasionally result in a good outcome if you are lucky; it is still a bad decision . Steps in Decision Theory 1. List the possible alternatives (actions/decisions) 2. …
DECISION MODELING USING EXCEL - Clemson University
decision modeling using excel updated for microsoft office xp august 2003 michael r. middleton
STUDENT’S PERFORMANCE ANALYSIS USING DECISION …
tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. 2.6 Decision Tree Algorithms A decision tree is …
Analysis: Problem analysis using a Problem & Solution Tree
The Problem tree then becomes the tool for helping to identify possible short- and long-term solutions/outcomes to the problems identified, otherwise known as a Solution tree. This …
THE JOURNAL OF AACE®INTERNATIONAL - Project Risk
Structure the Decision Tree A rigorous analysis of this decision using a simplified decision tree structure that minimizes our expected cost is shown as follows: • One contractor that we have …
Fault Tree Handbook with Aerospace Applications - NASA
Fault Tree Analysis (FTA) is one of the most important logic and probabilistic techniques used in PRA and system reliability assessment today. Methods to perform risk and reliability …
Fault Tree Analysis - Bosch Global
The analysis by means of a fault tree Fault status… • Needs a qualified moderator that methodically guides the team. • Requires a high level of discipline in preparing the fault tree to …
Lecture 9: Learning Decision Trees and DNFs 1 Two Important …
arbitrary parities of variables. Figure 2 contains an example of a function computable by decision tree on the parity of the various subsets of variables. Another example is parity function which …
Application of decision tree analysis and expected monetary …
Decision tree analysis and expected monetary values. 58 Tables Table 1. Risk evaluation techniques. 25 Table 2. Delphi process (Hasson, Keeney & McKenna, 2000). 32 Table 3. …
Chapter 8 Decision Analysis - hatemmasri.com
Risk Analysis and Sensitivity Analysis Decision Analysis with Sample Information Computing Branch Probabilities Problem Formulation A decision problem is characterized by decision …
